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Working with WFS/WMS Services

Source: vignettes/argentum.rmd
argentum.rmd

Understanding WFS and WMS Services

What are WFS and WMS?

WFS (Web Feature Service) and WMS (Web Map Service) are standardized protocols for serving georeferenced map data over the internet:

  • WFS provides access to actual geographic features with geometry and attributes
  • WMS provides map images rendered from geographic data

WFS Services in Detail

When you use Argentum to import WFS layers, you’re getting actual vector data that you can analyze and manipulate in R:

library(Argentum)
library(sf)

# Get organization data
org <- argentum_select_organization(search = "Buenos Aires")

# List available layers
layers <- argentum_list_layers(org$Name)

# Import a specific layer
sf_layer <- argentum_import_wfs_layer(org$WFS_URL, layers$Name[1])

# Now you can work with the data using sf functions
st_crs(sf_layer)  # Check the coordinate reference system
plot(sf_layer)    # Basic plot of the geometry

Working with Service Capabilities

Before importing data, you can check what capabilities a service offers:

# Get capabilities document
capabilities <- argentum_get_capabilities(org$WFS_URL)

# The capabilities document contains information about:
# - Available layers
# - Supported operations
# - Coordinate reference systems
# - Output formats

Best Practices

1. Error Handling

Always implement proper error handling:

tryCatch({
  # Attempt to import data
  sf_layer <- argentum_import_wfs_layer(org$WFS_URL, layers$Name[1])
}, error = function(e) {
  # Handle any errors that occur
  message("Error importing layer: ", e$message)
})

2. Performance Considerations

When working with WFS services:

# Use appropriate timeout values for large datasets
capabilities <- argentum_get_capabilities(
  url = org$WFS_URL,
  timeout = 60  # Increase timeout for slow connections
)

3. Data Processing

After importing WFS data:

library(sf)
library(dplyr)

# Check the data structure
str(sf_layer)

# Basic statistics
summary(sf_layer)

# Spatial operations
sf_layer_transformed <- st_transform(sf_layer, 4326)

# Calculate areas if working with polygons
if (all(st_geometry_type(sf_layer) %in% c("POLYGON", "MULTIPOLYGON"))) {
  sf_layer$area <- st_area(sf_layer)
}

Advanced Usage

Custom Queries

While Argentum provides high-level functions, you can also work with WFS services directly:

# Example of constructing a custom WFS URL
base_url <- org$WFS_URL
query_params <- list(
  service = "WFS",
  version = "1.1.0",
  request = "GetFeature",
  typeName = layers$Name[1],
  outputFormat = "application/json"
)

# Build the URL
query_url <- httr::modify_url(
  url = base_url,
  query = query_params
)

Troubleshooting

Common issues and solutions:

  1. Connection Timeouts
    • Increase timeout value
    • Check internet connection
    • Verify service availability
  2. Invalid Layer Names
    • Use argentum_list_layers() to get exact layer names
    • Check for case sensitivity
    • Verify layer still exists in service
  3. Data Format Issues
    • Check supported output formats
    • Verify coordinate reference systems
    • Ensure data compatibility with sf package

Future Development

Planned features for future versions:

  • Spatial filtering support
  • Temporal queries
  • WMS integration
  • Caching mechanism
  • Batch export capabilities

Session Information 

sessionInfo()
#> R version 4.3.1 (2023-06-16 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19045)
#> 
#> Matrix products: default
#> 
#> 
#> locale:
#> [1] LC_COLLATE=Spanish_Argentina.utf8  LC_CTYPE=Spanish_Argentina.utf8   
#> [3] LC_MONETARY=Spanish_Argentina.utf8 LC_NUMERIC=C                      
#> [5] LC_TIME=Spanish_Argentina.utf8    
#> 
#> time zone: America/Buenos_Aires
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.37     desc_1.4.3        R6_2.5.1          fastmap_1.2.0    
#>  [5] xfun_0.50         cachem_1.1.0      knitr_1.49        htmltools_0.5.8.1
#>  [9] rmarkdown_2.29    lifecycle_1.0.4   cli_3.6.1         sass_0.4.9       
#> [13] pkgdown_2.1.1     textshaping_0.4.1 jquerylib_0.1.4   systemfonts_1.2.0
#> [17] compiler_4.3.1    rstudioapi_0.17.1 tools_4.3.1       ragg_1.3.3       
#> [21] bslib_0.8.0       evaluate_1.0.3    yaml_2.3.10       jsonlite_1.8.9   
#> [25] rlang_1.1.1       fs_1.6.5          htmlwidgets_1.6.4