Social Progress Index

Overview

The Amazon Social Progress Index (IPS) is a comprehensive indicator framework that measures social and environmental progress in the Legal Amazon region. This collaborative initiative combines:

Imazon (Instituto do Homem e Meio Ambiente da Amazônia): Brazilian research organization
Social Progress Imperative: International organization focused on measuring societal well-being

This dataset captures:

Multi-dimensional development indicators: Spanning 8 domains of social and environmental progress
Municipality-level data: All Legal Amazon municipalities assessed
Quality of life metrics: Health, education, sanitation, infrastructure
Environmental indicators: Forest cover, deforestation risk, sustainability
Violence and safety: Public safety and security metrics
Temporal coverage: Data from 2014, 2018, 2021, 2023
Geographic coverage: 570+ municipalities across Legal Amazon

The IPS provides a holistic view of sustainable development, moving beyond simple economic measures (GDP) to encompass environmental sustainability and social well-being.

Data Source and Methodology

The Social Progress Index: - Based on 50+ individual indicators across 12 domains - Uses data from government agencies, NGOs, and research institutions - Aggregated into 3 main dimensions and 12 subdimensions - Indexed to 0-100 scale for comparability - Methodologically rigorous with transparent weighting

For detailed methodology, visit Social Progress Imperative.

Available Dimensions

The IPS framework includes 8 main dataset options:

1. all

Complete Social Progress Index with all dimensions and indicators.

Coverage: Comprehensive assessment across all domains
Variables: All indicators and index scores
Use cases: Holistic development analysis, overall progress tracking, multi-dimensional comparisons

2. life_quality

Indicators related to quality of life and well-being.

Variables: Healthcare quality, life expectancy, nutrition, shelter quality
Use cases: Health and wellness analysis, living standards assessment, healthcare quality evaluation

3. sanit_habit

Sanitation and habitat indicators.

Variables: Access to improved sanitation, water quality, housing conditions
Use cases: Infrastructure assessment, water and sanitation access analysis, housing quality evaluation

4. violence

Public safety and violence indicators.

Variables: Crime rates, safety perceptions, homicide data
Use cases: Public safety analysis, violence hotspot identification, security trends

5. educ

Education and literacy indicators.

Variables: School enrollment, literacy rates, educational attainment, quality of education
Use cases: Education access analysis, literacy trends, human capital assessment

6. communic

Communication and connectivity indicators.

Variables: Internet access, mobile phone coverage, communication infrastructure
Use cases: Digital divide analysis, connectivity assessment, tech adoption patterns

7. mortality

Health and mortality indicators.

Variables: Child mortality, maternal mortality, mortality rates by cause
Use cases: Health outcomes analysis, maternal/child health assessment, disease burden evaluation

8. deforest

Environmental and deforestation indicators.

Variables: Forest cover, deforestation rates, environmental sustainability
Use cases: Forest monitoring, environmental assessment, climate/conservation analysis

Function Parameters

1. dataset

Selects which dimension(s) to download.

dataset = "all"         # All dimensions
dataset = "life_quality" # Quality of life metrics
dataset = "sanit_habit"  # Sanitation and habitat
dataset = "violence"     # Public safety and violence
dataset = "educ"         # Education indicators
dataset = "communic"     # Communication and connectivity
dataset = "mortality"    # Health and mortality
dataset = "deforest"     # Environmental and deforestation

2. raw_data

Controls whether to download original or processed data.

TRUE: Returns raw data exactly as published
FALSE: Returns treated data with standardized English variable names and formatting

raw_data = FALSE  # logical

3. time_period

Specifies which assessment year(s) to download.

Available years: 2014, 2018, 2021, 2023

time_period = 2023              # Most recent
time_period = c(2018, 2023)     # Specific years
time_period = c(2014, 2018, 2021, 2023)  # Multiple years

4. language

Output language for variable names and labels.

"pt": Portuguese
"eng": English

language = "eng"  # character string

Examples

# download raw IPS data from 2014
data <- load_ips(
  dataset = "all",
  raw_data = TRUE,
  time_period = 2014,
  language = "eng"
)

# download treated deforestation IPS data from 2018 in portuguese
data <- load_ips(
  dataset = "deforest",
  raw_data = FALSE,
  time_period = 2018,
  language = "pt"
)

Data Notes

Index Scales

0-100 scale: All indices standardized to 0-100 for comparison
Higher is better: Across all dimensions except deforestation (where higher forest index = better)
Comparable across dimensions: Standardized scale allows cross-dimension comparison

Dimensions and Indicators

Each dimension contains multiple indicators: - Life quality: 4-6 indicators - Sanitation/habitat: 3-5 indicators
- Violence: 3-4 indicators - Education: 3-4 indicators - Communication: 2-3 indicators - Mortality: 3-4 indicators - Deforestation: 2-3 indicators

(Exact number varies by year and methodology)

Temporal Comparisons

When comparing across years (2014, 2018, 2021, 2023): - Methodology may have evolved between assessments - New indicators may have been added - Some municipalities may not have data in all years - Use caution comparing very old (2014) with recent (2023) data

Missing Data

Some municipalities may lack data for specific indicators
Remote or less accessible areas may have less complete data
Use na.rm = TRUE in aggregations to handle missing values

Geographic Coverage

Covers 570+ municipalities in the Legal Amazon
Includes all states with Amazon territory
Some frontier/protected areas may lack complete data