inst/doc/spatial-analysis.R

In geospatialsuite: Comprehensive Geospatiotemporal Analysis and Multimodal Integration Toolkit

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE,
  fig.width = 8,
  fig.height = 6
)

## ----setup--------------------------------------------------------------------
library(geospatialsuite)
library(terra)
library(sf)

# Check package functionality
test_package_minimal(verbose = TRUE)

## ----quick-map-examples, eval=FALSE-------------------------------------------
# # Create sample data for demonstration
# sample_points <- data.frame(
#   lon = c(-83.1, -83.2, -83.3, -82.9, -82.8),
#   lat = c(40.1, 40.2, 40.3, 40.0, 40.4),
#   ndvi = c(0.7, 0.8, 0.6, 0.75, 0.85),
#   yield = c(150, 180, 120, 160, 200)
# )
# 
# # One-line mapping with auto-detection
# quick_map(sample_points)
# 
# # Quick map with specific variable
# quick_map(sample_points, variable = "ndvi", title = "NDVI Distribution")
# 
# # Quick map with raster data (if available)
# # quick_map("path/to/raster.tif")

## ----point-mapping, eval=FALSE------------------------------------------------
# # Convert sample data to sf object
# sample_sf <- sf::st_as_sf(sample_points,
#                           coords = c("lon", "lat"),
#                           crs = 4326)
# 
# # Basic point map
# basic_map <- create_spatial_map(
#   spatial_data = sample_sf,
#   fill_variable = "ndvi",
#   map_type = "points",
#   title = "Sample NDVI Points",
#   verbose = TRUE
# )
# 
# print(basic_map)

## ----color-schemes, eval=FALSE------------------------------------------------
# # NDVI-specific colors
# ndvi_map <- create_spatial_map(
#   spatial_data = sample_sf,
#   fill_variable = "ndvi",
#   color_scheme = "ndvi",
#   title = "NDVI with Specialized Colors"
# )
# 
# # Terrain colors for elevation data
# # terrain_map <- create_spatial_map(
# #   spatial_data = elevation_data,
# #   color_scheme = "terrain",
# #   title = "Elevation Map"
# # )
# 
# # Plasma colors for general data
# plasma_map <- create_spatial_map(
#   spatial_data = sample_sf,
#   fill_variable = "yield",
#   color_scheme = "plasma",
#   title = "Yield with Plasma Colors"
# )
# 
# print(plasma_map)

## ----raster-mapping, eval=FALSE-----------------------------------------------
# # Create sample raster for demonstration
# sample_raster <- terra::rast(nrows = 50, ncols = 50,
#                              xmin = -84, xmax = -82,
#                              ymin = 39, ymax = 41)
# terra::values(sample_raster) <- runif(2500, 0.2, 0.9)
# names(sample_raster) <- "NDVI"
# 
# # Fast raster plot
# plot_raster_fast(
#   raster_data = sample_raster,
#   title = "Sample NDVI Raster",
#   color_scheme = "ndvi"
# )
# 
# # With custom breaks
# plot_raster_fast(
#   raster_data = sample_raster,
#   title = "NDVI with Custom Classes",
#   color_scheme = "ndvi",
#   breaks = c(0, 0.3, 0.5, 0.7, 1.0)
# )

## ----rgb-composites, eval=FALSE-----------------------------------------------
# # Create sample multi-band raster
# red_band <- terra::rast(nrows = 30, ncols = 30,
#                         xmin = -84, xmax = -82,
#                         ymin = 39, ymax = 41)
# terra::values(red_band) <- runif(900, 0.1, 0.3)
# 
# green_band <- red_band
# terra::values(green_band) <- runif(900, 0.2, 0.4)
# 
# blue_band <- red_band
# terra::values(blue_band) <- runif(900, 0.05, 0.15)
# 
# # Stack bands
# rgb_stack <- c(red_band, green_band, blue_band)
# names(rgb_stack) <- c("Red", "Green", "Blue")
# 
# # RGB composite plot
# plot_rgb_raster(
#   raster_data = rgb_stack,
#   r = 1, g = 2, b = 3,
#   stretch = "lin",
#   title = "RGB Composite"
# )
# 
# # False color composite
# plot_rgb_raster(
#   raster_data = rgb_stack,
#   r = 2, g = 1, b = 3,  # Green-Red-Blue
#   stretch = "hist",
#   title = "False Color Composite"
# )

## ----interactive-maps, eval=FALSE---------------------------------------------
# # Interactive point map (requires leaflet package)
# if (requireNamespace("leaflet", quietly = TRUE)) {
#   interactive_map <- create_interactive_map(
#     spatial_data = sample_sf,
#     fill_variable = "ndvi",
#     basemap = "terrain",
#     title = "Interactive NDVI Map"
#   )
# 
#   # Save interactive map
#   # htmlwidgets::saveWidget(interactive_map, "interactive_ndvi.html")
# }
# 
# # Interactive mapping with custom basemap
# if (requireNamespace("leaflet", quietly = TRUE)) {
#   satellite_map <- create_interactive_map(
#     spatial_data = sample_sf,
#     fill_variable = "yield",
#     basemap = "satellite",
#     title = "Yield on Satellite Imagery"
#   )
# }

## ----regional-boundaries, eval=FALSE------------------------------------------
# # Simulate Ohio boundary for demonstration
# ohio_boundary <- sf::st_polygon(list(matrix(c(
#   -84.5, 38.5, -80.5, 38.5, -80.5, 42.0, -84.5, 42.0, -84.5, 38.5
# ), ncol = 2, byrow = TRUE)))
# ohio_sf <- sf::st_sf(geometry = sf::st_sfc(ohio_boundary, crs = 4326))
# 
# # Map with region boundary
# map_with_boundary <- create_spatial_map(
#   spatial_data = sample_sf,
#   fill_variable = "ndvi",
#   region_boundary = ohio_sf,  # Would normally use "Ohio"
#   title = "NDVI in Ohio",
#   color_scheme = "ndvi"
# )
# 
# print(map_with_boundary)
# 
# # The package supports many boundary types:
# # create_spatial_map(data, region_boundary = "Ohio")          # US State
# # create_spatial_map(data, region_boundary = "CONUS")         # Continental US
# # create_spatial_map(data, region_boundary = "Ohio:Franklin") # State:County
# # create_spatial_map(data, region_boundary = c(-84, 39, -82, 41)) # Bounding box

## ----comparison-maps, eval=FALSE----------------------------------------------
# # Create "before" and "after" rasters
# before_raster <- terra::rast(nrows = 30, ncols = 30,
#                              xmin = -84, xmax = -82,
#                              ymin = 39, ymax = 41)
# terra::values(before_raster) <- runif(900, 0.3, 0.7)
# names(before_raster) <- "NDVI_Before"
# 
# after_raster <- before_raster
# terra::values(after_raster) <- terra::values(before_raster) + runif(900, -0.1, 0.2)
# names(after_raster) <- "NDVI_After"
# 
# # Side-by-side comparison
# create_comparison_map(
#   data1 = before_raster,
#   data2 = after_raster,
#   comparison_type = "side_by_side",
#   titles = c("Before Treatment", "After Treatment"),
#   color_scheme = "ndvi"
# )
# 
# # Difference map
# create_comparison_map(
#   data1 = before_raster,
#   data2 = after_raster,
#   comparison_type = "difference",
#   titles = c("Before", "After"),
#   color_scheme = "RdBu"
# )

## ----custom-colors, eval=FALSE------------------------------------------------
# # Get available color schemes
# color_schemes <- c("viridis", "plasma", "ndvi", "terrain", "water", "categorical")
# 
# # Apply different schemes to the same data
# for (scheme in color_schemes[1:3]) {
#   map <- create_spatial_map(
#     spatial_data = sample_sf,
#     fill_variable = "ndvi",
#     color_scheme = scheme,
#     title = paste("NDVI with", scheme, "colors"),
#     point_size = 4
#   )
# 
#   print(paste("Created map with", scheme, "color scheme"))
# }

## ----map-styling, eval=FALSE--------------------------------------------------
# # Customize point appearance
# styled_map <- create_spatial_map(
#   spatial_data = sample_sf,
#   fill_variable = "yield",
#   map_type = "points",
#   point_size = 6,
#   color_scheme = "plasma",
#   title = "Customized Point Map"
# )
# 
# print(styled_map)
# 
# # Map with transparent points
# # (Demonstrated conceptually - actual implementation may vary)

## ----publication-maps, eval=FALSE---------------------------------------------
# # Create high-resolution map for publication
# publication_map <- create_spatial_map(
#   spatial_data = sample_sf,
#   fill_variable = "ndvi",
#   color_scheme = "ndvi",
#   title = "NDVI Distribution in Study Area",
#   output_file = "publication_ndvi_map.png"
# )
# 
# # Customize for journal specifications
# journal_map <- create_spatial_map(
#   spatial_data = sample_sf,
#   fill_variable = "yield",
#   color_scheme = "viridis",
#   title = "",  # No title for journal figure
#   output_file = "figure_1.png"
# )

## ----map-layout, eval=FALSE---------------------------------------------------
# # The package automatically handles legends and layouts
# # Maps include appropriate legends, scale bars, and formatting
# 
# # For complex layouts, combine with other packages:
# # library(patchwork)  # For multi-panel figures
# # library(ggplot2)    # For additional customization

## ----error-handling, eval=FALSE-----------------------------------------------
# # Test data validation
# tryCatch({
#   # This should work
#   valid_map <- create_spatial_map(sample_sf, fill_variable = "ndvi")
#   print("Valid map created successfully")
# }, error = function(e) {
#   print(paste("Error:", e$message))
# })
# 
# # Handle missing data
# sample_with_na <- sample_sf
# sample_with_na$ndvi[1:2] <- NA
# 
# na_map <- create_spatial_map(
#   spatial_data = sample_with_na,
#   fill_variable = "ndvi",
#   title = "Data with Missing Values"
# )
# 
# print("Map with NA values handled automatically")

## ----diagnostics, eval=FALSE--------------------------------------------------
# # Quick diagnostic check
# diagnostic_result <- quick_diagnostic()
# 
# # Test specific mapping functions
# mapping_test <- tryCatch({
#   test_map <- create_spatial_map(sample_sf, fill_variable = "ndvi")
#   "Mapping functions working"
# }, error = function(e) {
#   paste("Mapping error:", e$message)
# })
# 
# print(mapping_test)

## ----data-prep, eval=FALSE----------------------------------------------------
# # Always check your data first
# print("Sample data structure:")
# print(head(sample_sf))
# 
# # Verify coordinate reference system
# print(paste("CRS:", sf::st_crs(sample_sf)$input))
# 
# # Check for valid geometries
# valid_geoms <- sf::st_is_valid(sample_sf)
# print(paste("Valid geometries:", all(valid_geoms)))

## ----progressive-enhancement, eval=FALSE--------------------------------------
# # Start simple, then add complexity
# simple_map <- quick_map(sample_sf)
# 
# # Add customization progressively
# enhanced_map <- create_spatial_map(
#   spatial_data = sample_sf,
#   fill_variable = "ndvi",
#   color_scheme = "ndvi",
#   title = "Enhanced NDVI Map",
#   point_size = 5
# )
# 
# # Add interactivity if needed
# # interactive_version <- create_interactive_map(sample_sf, fill_variable = "ndvi")

## ----performance, eval=FALSE--------------------------------------------------
# # For large datasets, consider:
# # 1. Simplifying geometries
# # 2. Reducing point density
# # 3. Using raster instead of vector for very dense data
# 
# # Example: Check data size
# print(paste("Number of features:", nrow(sample_sf)))
# print(paste("Number of variables:", ncol(sample_sf) - 1))  # Minus geometry
# 
# # For large rasters, use terra's efficient plotting
# if (exists("sample_raster")) {
#   print(paste("Raster dimensions:", paste(dim(sample_raster), collapse = " x ")))
# }

## ----ggplot-integration, eval=FALSE-------------------------------------------
# # GeoSpatialSuite maps work well with ggplot2
# library(ggplot2)
# 
# # Extract ggplot object for further customization
# base_map <- create_spatial_map(sample_sf, fill_variable = "ndvi")
# 
# # Customize with ggplot2 (if the map is a ggplot object)
# if (inherits(base_map, "ggplot")) {
#   enhanced_ggplot <- base_map +
#     theme_minimal() +
#     labs(caption = "Data source: Field measurements") +
#     theme(
#       plot.title = element_text(size = 16, face = "bold"),
#       legend.position = "bottom"
#     )
# }

## ----leaflet-integration, eval=FALSE------------------------------------------
# # For interactive web maps
# if (requireNamespace("leaflet", quietly = TRUE)) {
#   web_map <- create_interactive_map(
#     spatial_data = sample_sf,
#     fill_variable = "ndvi",
#     popup_vars = c("ndvi", "yield"),
#     basemap = "terrain"
#   )
# 
#   # Further customize with leaflet functions
#   enhanced_web_map <- web_map %>%
#     leaflet::addMiniMap() %>%
#     leaflet::addScaleBar()
# }

## ----ndvi-mapping, eval=FALSE-------------------------------------------------
# # Create sample NDVI raster
# ndvi_raster <- terra::rast(nrows = 40, ncols = 40,
#                            xmin = -84, xmax = -82,
#                            ymin = 39, ymax = 41)
# terra::values(ndvi_raster) <- runif(1600, 0.1, 0.9)
# names(ndvi_raster) <- "NDVI"
# 
# # Specialized NDVI visualization
# ndvi_map <- create_ndvi_map(
#   ndvi_data = ndvi_raster,
#   title = "NDVI Analysis",
#   ndvi_classes = "none"  # Use continuous colors
# )
# 
# print("NDVI map created with specialized colors")

## ----water-mapping, eval=FALSE------------------------------------------------
# # Create sample water quality data
# water_points <- data.frame(
#   lon = c(-83.0, -83.1, -83.2, -82.9, -82.8),
#   lat = c(40.0, 40.1, 40.2, 39.9, 40.3),
#   dissolved_oxygen = c(8.2, 7.5, 6.8, 8.9, 7.1),
#   temperature = c(18.5, 19.2, 20.1, 17.8, 19.5)
# )
# 
# water_sf <- sf::st_as_sf(water_points,
#                          coords = c("lon", "lat"),
#                          crs = 4326)
# 
# # Water quality visualization
# water_map <- create_water_quality_plot(
#   water_data = water_sf,
#   variable = "dissolved_oxygen",
#   title = "Dissolved Oxygen Levels"
# )

## ----static-export, eval=FALSE------------------------------------------------
# # High-resolution PNG export
# create_spatial_map(
#   spatial_data = sample_sf,
#   fill_variable = "ndvi",
#   color_scheme = "ndvi",
#   title = "NDVI Distribution",
#   output_file = "high_res_ndvi.png"
# )
# 
# # PDF export for vector graphics
# create_spatial_map(
#   spatial_data = sample_sf,
#   fill_variable = "yield",
#   color_scheme = "viridis",
#   title = "Yield Distribution",
#   output_file = "yield_map.pdf"
# )

## ----interactive-export, eval=FALSE-------------------------------------------
# # Export interactive map as HTML
# if (requireNamespace("leaflet", quietly = TRUE)) {
#   interactive_map <- create_interactive_map(
#     spatial_data = sample_sf,
#     fill_variable = "ndvi",
#     title = "Interactive NDVI Map"
#   )
# 
#   # Save as HTML file
#   if (requireNamespace("htmlwidgets", quietly = TRUE)) {
#     htmlwidgets::saveWidget(
#       interactive_map,
#       "interactive_ndvi_map.html",
#       selfcontained = TRUE
#     )
#   }
# }

## ----multi-layer, eval=FALSE--------------------------------------------------
# # Create comparison visualization
# create_comparison_map(
#   data1 = sample_raster,
#   data2 = sample_raster * 1.2,  # Simulated change
#   comparison_type = "side_by_side",
#   titles = c("Year 1", "Year 2"),
#   color_scheme = "ndvi"
# )

## ----auto-detection, eval=FALSE-----------------------------------------------
# # The package automatically detects appropriate mapping approaches
# 
# # Point data -> scatter plot with colors
# point_auto <- create_spatial_map(
#   spatial_data = sample_sf,
#   fill_variable = "ndvi",
#   map_type = "auto"  # Auto-detects as points
# )
# 
# # Raster data -> raster plot
# raster_auto <- create_spatial_map(
#   spatial_data = sample_raster,
#   map_type = "auto"  # Auto-detects as raster
# )
# 
# print("Auto-detection completed successfully")

## ----performance-tips, eval=FALSE---------------------------------------------
# # 1. Use terra plotting for speed
# plot_raster_fast(sample_raster, title = "Fast Plotting")
# 
# # 2. Simplify data when appropriate
# # simplified_sf <- sf::st_simplify(complex_sf, dTolerance = 100)
# 
# # 3. Use appropriate map types
# # For very dense points, consider raster interpolation
# # For large rasters, consider aggregation
# 
# # 4. Monitor memory usage
# print(paste("Sample data memory usage:",
#             format(object.size(sample_sf), units = "KB")))

## ----memory-management, eval=FALSE--------------------------------------------
# # Clean up large objects when done
# # rm(large_raster)
# # gc()  # Garbage collection
# 
# # Use temporary files for intermediate results
# temp_file <- tempfile(fileext = ".tif")
# print(paste("Temporary file:", temp_file))
# 
# # For very large analyses, process in chunks
# # chunk_size <- 1000  # Adjust based on available memory

## ----crs-troubleshooting, eval=FALSE------------------------------------------
# # Check CRS compatibility
# sample_utm <- sf::st_transform(sample_sf, crs = 3857)  # Web Mercator
# 
# # The package handles CRS automatically in most cases
# mixed_crs_map <- create_spatial_map(
#   spatial_data = sample_utm,  # UTM coordinates
#   fill_variable = "ndvi",
#   title = "Map with Different CRS"
# )
# 
# print("CRS handling completed automatically")

## ----format-troubleshooting, eval=FALSE---------------------------------------
# # Test with different data formats
# formats_test <- list(
#   sf_object = sample_sf,
#   data_frame = sample_points,
#   raster_object = sample_raster
# )
# 
# for (format_name in names(formats_test)) {
#   tryCatch({
#     test_map <- quick_map(formats_test[[format_name]])
#     print(paste(format_name, "format: OK"))
#   }, error = function(e) {
#     print(paste(format_name, "format error:", e$message))
#   })
# }