legal_amazon

Overview

The municipalities dataset is a foundational reference dataset included in the datazoom.amazonia package. It contains key information about all Brazilian municipalities and identifies which are located in (or overlapping with) the Legal Amazon region.

This dataset includes:

The municipalities dataset is essential infrastructure for this package—most package functions that return geographic data match results to this municipalities reference to enable Legal Amazon filtering and consistent geographic identification.

Data Source

The municipalities dataset is compiled from: - IBGE: Brazilian Institute of Geography and Statistics official municipal data - Legal Amazon definition: Based on official Brazilian government definition - Spatial boundaries: IBGE 2019 municipal boundary shapefile - Maintained by: datazoom.amazonia package developers

Dataset Structure

The municipalities dataset contains the following information:

Key Variables

Data Types

Accessing the Dataset

In R Code

# Load Brazilian municipalities dataset
data <- datazoom.amazonia::municipalities

# Or after loading the package
library(datazoom.amazonia)
data <- municipalities

# View structure
str(municipalities)
head(municipalities)

# Filter for Legal Amazon municipalities only
amazon_municipalities <- municipalities %>%
  filter(legal_amazon == TRUE)

What It Contains

The dataset includes all 5,570+ Brazilian municipalities with: - Official IBGE identification codes - State and region classification - Legal Amazon flag for filtering - Spatial geometries for geographic analysis

Common Use Cases

Use Case 2: Partial Amazon Municipalities

Important: Some municipalities are only partially within the Legal Amazon.

For statistics reported at municipality level in this package: - Partial Amazon municipalities: Data is reported for only the Amazon-included portion - Identification: The municipalities dataset identifies these cases - Interpretation: When a municipality is partially in Amazon, reported statistics reflect the Amazon portion only

# Identify fully vs. partially included municipalities
full_amazon <- municipalities %>%
  filter(legal_amazon == TRUE & fully_included == TRUE)

partial_amazon <- municipalities %>%
  filter(legal_amazon == TRUE & fully_included == FALSE)

print(paste("Fully in Amazon:", nrow(full_amazon)))
print(paste("Partially in Amazon:", nrow(partial_amazon)))

Key Characteristics

Municipal Coverage

Partial Inclusion

Important note on municipality-level data: - Partial municipalities: Some municipalities extend beyond Legal Amazon boundaries - Data reporting: When data is reported at municipality level for partial municipalities, it reflects only the Legal Amazon portion - Identification: This dataset identifies which municipalities are partial

# Check for partial municipalities
partial_check <- municipalities %>%
  filter(legal_amazon == TRUE) %>%
  filter(!is.na(amazon_percentage)) %>%
  filter(amazon_percentage < 100)

if (nrow(partial_check) > 0) {
  print("Municipalities partially in Legal Amazon:")
  print(partial_check)
}

Important Notes

Boundary Changes

Brazilian municipalities occasionally undergo changes: - New municipalities: Created from existing ones (last major change 2021) - Boundary adjustments: IBGE periodically refines municipal boundaries - Historical data: When comparing very old data with recent data, be aware municipalities may have been reorganized

Code Consistency

Always use IBGE municipality codes (not names) when: - Merging multiple datasets - Doing time-series analysis - Comparing across sources - Municipality names may change or be ambiguous; codes are unique and stable

Spatial Data

The municipalities dataset includes spatial boundaries (when loaded as SF object): - Format: Simple features (SF) polygons - CRS: WGS84 (EPSG:4326) - Use: Spatial operations, mapping, spatial joins with other geographic data

Accessing Spatial Data

For Mapping and Spatial Analysis

library(sf)
library(ggplot2)

# Load municipalities with geometry
municipalities_sf <- municipalities %>%
  st_as_sf()  # If not already SF format

# Map Legal Amazon
amazon_map <- municipalities_sf %>%
  filter(legal_amazon == TRUE)

ggplot(amazon_map) +
  geom_sf(fill = "lightgreen", color = "darkgreen") +
  labs(title = "Legal Amazon Municipalities") +
  theme_minimal()

# Spatial operations example: count municipalities by state
munic_by_state <- municipalities_sf %>%
  group_by(state) %>%
  summarize(
    num_municipalities = n(),
    total_area_km2 = sum(st_area(.), na.rm = TRUE) / 1e6,
    .groups = 'drop'
  )

Troubleshooting

Matching Issues

Problem: Municipality names don’t match between datasets Solution: Use IBGE municipality codes instead of names for joining data

Problem: Some municipalities missing after filtering Solution: Check for name spelling variations; use code-based matching

Geographic Analysis

Problem: Spatial operations are slow Solution: Simplify geometries (st_simplify()) or work with state/region level first

Problem: Mapping appears incorrect Solution: Verify CRS (should be WGS84); check for invalid geometries (st_is_valid())

Data Integration

Problem: Aggregating municipality data to regions Solution: Use left_join() with municipalities dataset to add region information

Problem: Partial municipalities causing data discrepancies Solution: Account for partial Amazon municipalities; some statistics are reported for Amazon portion only