Creating Maps and Plots

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2025-12-17

Overview

The manureshed package creates several types of visualizations:

Classification maps - Show nutrient balance categories
WWTP facility maps - Show treatment plant locations and sizes
Influence maps - Show how much WWTP contributes to each area
Comparison plots - Before/after adding WWTP data
Network plots - Show transitions between neighboring areas

Quick Maps with quick_analysis()

The easiest way to get maps is with quick_analysis():

# This creates all maps automatically
results <- quick_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE,
  output_dir = "my_maps"
)

# Maps are saved in the output directory

Step-by-Step Map Creation

1. Get Analysis Results First

# Run analysis to get data for mapping
results <- run_builtin_analysis(
  scale = "county",
  year = 2016, 
  nutrients = c("nitrogen", "phosphorus"),
  include_wwtp = TRUE
)

2. Agricultural Classification Maps

# Basic nitrogen map
nitrogen_map <- map_agricultural_classification(
  data = results$agricultural,
  nutrient = "nitrogen",
  classification_col = "N_class", 
  title = "Agricultural Nitrogen Classifications"
)

# View the map
print(nitrogen_map)

# Save the map
save_plot(nitrogen_map, "nitrogen_classes.png", width = 10, height = 8)

# Phosphorus map
phosphorus_map <- map_agricultural_classification(
  data = results$agricultural,
  nutrient = "phosphorus",
  classification_col = "P_class",
  title = "Agricultural Phosphorus Classifications" 
)

3. Combined Maps (Agricultural + WWTP)

# Map showing effect of adding WWTP data
combined_nitrogen <- map_agricultural_classification(
  data = results$integrated$nitrogen,
  nutrient = "nitrogen",
  classification_col = "combined_N_class",
  title = "Nitrogen with WWTP Integration"
)

combined_phosphorus <- map_agricultural_classification(
  data = results$integrated$phosphorus, 
  nutrient = "phosphorus",
  classification_col = "combined_P_class",
  title = "Phosphorus with WWTP Integration"
)

4. WWTP Facility Maps

# Map showing WWTP locations colored by size
facility_map <- map_wwtp_points(
  wwtp_sf = results$wwtp$nitrogen$spatial_data,
  nutrient = "nitrogen",
  title = "Nitrogen WWTP Facilities"
)

print(facility_map)

5. WWTP Influence Maps

# Map showing how much WWTP contributes to each area
influence_map <- map_wwtp_influence(
  data = results$integrated$nitrogen,
  nutrient = "nitrogen",
  title = "WWTP Contribution to Total Nitrogen"
)

print(influence_map)

Comparison Plots

Before/After WWTP Integration

# Create summary data
summary_data <- create_classification_summary(
  data = results$integrated$nitrogen,
  agricultural_col = "N_class",
  combined_col = "combined_N_class"
)

# Before/after bar chart
comparison_plot <- plot_before_after_comparison(
  data = summary_data,
  nutrient = "nitrogen",
  title = "Effect of Adding WWTP Data"
)

print(comparison_plot)

# Impact ratios
impact_plot <- plot_impact_ratios(
  data = summary_data,
  title = "WWTP Impact on Classifications"
)

# Absolute changes
change_plot <- plot_absolute_changes(
  data = summary_data,
  title = "Change in Number of Counties"
)

Network Plots

Spatial Transition Networks

# Add coordinates to the data
centroids <- add_centroid_coordinates(results$integrated$nitrogen)

# Calculate how often different classes are next to each other
transitions <- calculate_transition_probabilities(
  centroids, "combined_N_class"
)

# Create network plot
create_network_plot(
  transition_df = transitions,
  nutrient = "nitrogen",
  analysis_type = "WWTP + Agricultural", 
  output_path = "nitrogen_network.png"
)

# View the transition table
print(transitions)

Customizing Maps

Save Options

# Different resolutions and formats
save_plot(nitrogen_map, "map_web.png", width = 8, height = 6, dpi = 150)      # Web
save_plot(nitrogen_map, "map_print.png", width = 10, height = 8, dpi = 300)   # Print  
save_plot(nitrogen_map, "map_publication.png", width = 12, height = 9, dpi = 600) # Publication

# Vector formats
save_plot(nitrogen_map, "map_vector.pdf", width = 10, height = 8)

Custom Colors

# Use different colors
custom_map <- map_agricultural_classification(
  data = results$agricultural,
  nutrient = "nitrogen",
  classification_col = "N_class",
  title = "Custom Colors"
) +
  ggplot2::scale_fill_manual(
    values = c("Source" = "red", "Sink_Deficit" = "blue", 
               "Sink_Fertilizer" = "green", "Within_County" = "yellow",
               "Excluded" = "gray"),
    labels = c("Source", "Sink Deficit", "Sink Fertilizer", 
               "Within County", "Excluded")
  )

Working with Different Scales

County Level

# County analysis
county_results <- run_builtin_analysis(scale = "county", year = 2016, 
                                       nutrients = "nitrogen", include_wwtp = TRUE)

county_map <- map_agricultural_classification(
  county_results$agricultural, "nitrogen", "N_class",
  "County-Level Nitrogen Classifications"
)

HUC8 Watersheds

# Watershed analysis  
huc8_results <- run_builtin_analysis(scale = "huc8", year = 2016,
                                     nutrients = "nitrogen", include_wwtp = TRUE)

huc8_map <- map_agricultural_classification(
  huc8_results$agricultural, "nitrogen", "N_class", 
  "Watershed-Level Nitrogen Classifications"
)

HUC2 Regions

# Regional analysis
huc2_results <- run_builtin_analysis(scale = "huc2", year = 2016,
                                     nutrients = "nitrogen", include_wwtp = TRUE)

huc2_map <- map_agricultural_classification(
  huc2_results$agricultural, "nitrogen", "N_class",
  "Regional-Level Nitrogen Classifications"
)

State-Specific Maps

# Create maps for a specific state
iowa_results <- run_state_analysis(
  state = "IA", 
  scale = "county",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

iowa_map <- map_agricultural_classification(
  iowa_results$agricultural, "nitrogen", "N_class",
  "Iowa Nitrogen Classifications"
)

# Quick state maps
texas_maps <- quick_state_analysis(
  state = "TX",
  scale = "huc8", 
  year = 2015,
  nutrients = "phosphorus",
  create_maps = TRUE
)

Multi-Panel Figures

# Create side-by-side comparison
library(ggplot2)
library(gridExtra)  # or cowplot

# Create two maps
map1 <- map_agricultural_classification(
  results$agricultural, "nitrogen", "N_class", "Agricultural Only"
)

map2 <- map_agricultural_classification(
  results$integrated$nitrogen, "nitrogen", "combined_N_class", "With WWTP"
)

# Combine them
combined_figure <- grid.arrange(map1, map2, ncol = 2)

# Save combined figure
ggsave("combined_maps.png", combined_figure, width = 16, height = 8)

Tips for Good Maps

Map Quality

# For presentations (screen)
save_plot(map, "presentation.png", width = 12, height = 8, dpi = 150)

# For reports (print)  
save_plot(map, "report.png", width = 10, height = 8, dpi = 300)

# For journals (high quality)
save_plot(map, "journal.png", width = 8, height = 6, dpi = 600)

Color Choices

Use the built-in colors for consistency
Consider colorblind-friendly palettes
Make sure colors show up in grayscale
Use high contrast for important categories

File Management

# Organize your outputs
create_maps_folder <- function(analysis_name) {
  dir.create(analysis_name, showWarnings = FALSE)
  dir.create(file.path(analysis_name, "maps"), showWarnings = FALSE)
  dir.create(file.path(analysis_name, "plots"), showWarnings = FALSE)
  dir.create(file.path(analysis_name, "data"), showWarnings = FALSE)
}

create_maps_folder("nitrogen_analysis_2016")

Troubleshooting

Common Issues

# If maps are blank, check your data
quick_check(results)

# If colors are wrong, check classification column names
table(results$agricultural$N_class)

# If coordinates are missing
centroids <- add_centroid_coordinates(results$agricultural)

# If maps are too slow, try smaller scale or fewer years

This covers the essential mapping and visualization functions in manureshed. The package makes it easy to create publication-quality maps and plots for nutrient flow analysis.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.