R for Green Architecture: Data-Driven Design in Informal Settlements
The Challenge: Sustainable Design in Resource-Limited Contexts
Urban informal settlements house a significant and growing share of the world’s urban population. These communities face compounding environmental challenges: flooding, heat stress, poor air quality, water scarcity, and inadequate sanitation. Green architecture — environmentally conscious design that maximises human welfare while minimising environmental impact — offers promising solutions, but implementation requires data-driven planning that accounts for the unique constraints of informal settlement contexts.
R programming, with its statistical depth and geospatial capabilities, provides a powerful analytical framework for understanding these challenges and designing effective interventions.
Why Data Matters in Informal Settlements
Formal urban development benefits from extensive data: cadastral records, utility maps, building permits, and environmental monitoring networks. Informal settlements typically lack all of these. Planning green infrastructure in data-scarce environments requires creative analytical approaches — exactly where R’s ecosystem excels.
R enables practitioners to:
- Map and analyse settlement patterns using satellite imagery and spatial data, even without official documentation
- Identify environmental vulnerabilities through statistical modelling and spatial analysis
- Optimise resource distribution using mathematical optimisation
- Establish rigorous evaluation frameworks for measuring intervention effectiveness
Use Case 1: Spatial Analysis for Green Infrastructure Planning
Determining where to place green infrastructure (community gardens, bio-swales, tree planting, green roofs) requires understanding complex spatial relationships. We use sf and leaflet to model site suitability based on multiple factors:
library(sf)
library(leaflet)
# Score potential sites based on multiple criteria
sites <- potential_locations |>
mutate(
suitability = 0.3 * drainage_score +
0.25 * access_score +
0.2 * sunlight_score +
0.15 * soil_quality +
0.1 * community_proximity,
priority = case_when(
suitability > 0.7 ~ "High",
suitability > 0.4 ~ "Medium",
TRUE ~ "Low"
)
)
# Interactive mapping for stakeholder engagement
leaflet(sites) |>
addTiles() |>
addCircleMarkers(
color = ~priority_palette(priority),
popup = ~paste("Type:", infrastructure_type,
"<br>Score:", round(suitability, 2))
)Interactive maps are particularly valuable for stakeholder engagement in communities where technical reports may not be accessible. A visual tool that residents can interact with drives better participation and more informed decision-making.
Use Case 2: Climate Vulnerability and Green Roof Modelling
Informal settlements experience disproportionate climate risks. Green roofs offer a relatively low-cost intervention for heat reduction, but planners need to understand the cost-benefit tradeoffs at different scales.
We model the relationship between green roof coverage and temperature reduction, then estimate the economic implications:
library(ggplot2)
# Model temperature reduction vs coverage
coverage_model <- tibble(
coverage_pct = seq(0, 100, by = 5),
temp_reduction = 5 * (coverage_pct / 100), # Simplified linear model
avoided_heat_days = temp_reduction * 8, # Days above threshold
cost_per_sqm = 45 - 0.15 * coverage_pct # Economies of scale
)
ggplot(coverage_model, aes(coverage_pct, temp_reduction)) +
geom_line(color = "#14b8a6", linewidth = 1.2) +
labs(
title = "Green Roof Coverage vs Temperature Reduction",
x = "Coverage (%)",
y = "Temperature Reduction (°C)"
)The analysis typically reveals diminishing cost-efficiency returns at higher coverage percentages — helping planners identify optimal intervention scales that maximise impact per unit of investment.
Use Case 3: Water Resource Optimisation
Many settlements experience simultaneous water scarcity during dry periods and flooding during rains. Rainwater harvesting systems can address both, but optimal tank sizing requires statistical modelling of rainfall patterns:
library(forecast)
# Model rainfall and harvesting potential
rainfall_model <- rainfall_data |>
group_by(month) |>
summarise(
mean_rain = mean(rainfall_mm),
p25 = quantile(rainfall_mm, 0.25),
p75 = quantile(rainfall_mm, 0.75)
)
# Optimise tank capacity for reliability
tank_optimisation <- tibble(
capacity_litres = seq(500, 10000, by = 500)
) |>
mutate(
reliability = map_dbl(capacity_litres, ~simulate_tank(., rainfall_data)),
cost = capacity_litres * cost_per_litre
)Use Case 4: Air Quality and Vegetation Impact
Informal settlements frequently suffer from poor air quality. We use spatial correlation analysis to quantify the relationship between vegetation cover (measured by NDVI from satellite imagery) and particulate matter concentrations:
library(terra)
# Correlate vegetation with air quality
ndvi <- rast("settlement_ndvi.tif")
pm25 <- rast("pm25_measurements.tif")
correlation <- layerCor(c(ndvi, pm25), fun = "pearson")This analysis consistently shows that increased vegetation significantly reduces particulate pollution — providing an evidence base for prioritising tree planting and green space creation in the most polluted areas.
The Broader Vision
R enables a level of analytical rigour in informal settlement planning that was previously accessible only to well-resourced formal development projects. By making these tools open and reproducible, we aim to democratise evidence-based environmental planning for the communities that need it most.
This work sits at the intersection of our environmental data science practice and our commitment to using technology for social impact. The same spatial analysis capabilities we use for EIA assessments and biodiversity monitoring translate directly to urban environmental challenges — different domain, same engineering discipline.