Leveraging R for Green Architecture in Slum Areas: Data-Driven Solutions for Sustainable Urban Development

R Programming
Sustainability
Urban Development
Green Architecture
Data Analysis
Social Impact
Exploring how R programming enables data-driven approaches to developing sustainable, climate-resilient green architecture in informal settlements and slum areas
Author

Kwizera Jean

Published

November 7, 2025

By Kwizera Jean, Kwiz Computing Technologies • November 7, 2025 • 17 min read

Introduction

As the world’s urban population continues to grow, informal settlements and slum areas face unprecedented challenges: inadequate housing, poor sanitation, limited access to clean water and vulnerability to climate change. Yet, these communities also represent opportunities for innovative, sustainable development. Green architecture—design that minimizes environmental impact while maximizing human well-being—offers a pathway forward, but its implementation in resource-constrained settings requires careful planning backed by robust data analysis.

This is where R, the powerful statistical programming language, becomes an invaluable tool. From spatial analysis and environmental monitoring to resource optimization and community health assessment, R provides architects, urban planners and development organizations with the analytical capabilities needed to design effective, context-appropriate green architecture solutions for slum areas.

Understanding the Challenge: Why Data Matters in Informal Settlements

Slum areas often lack the formal documentation and systematic data collection found in planned urban developments. This data scarcity makes traditional planning approaches difficult. However, R’s capabilities in working with diverse data sources—from satellite imagery and sensor networks to community surveys and open data initiatives—enable practitioners to:

  • Map and analyze informal settlement patterns without relying solely on official records
  • Identify environmental vulnerabilities through spatial and temporal analysis
  • Optimize resource allocation using statistical modeling
  • Monitor intervention outcomes with rigorous evaluation frameworks

Use Case 1: Spatial Analysis for Green Infrastructure Planning

The Challenge

Determining optimal locations for green infrastructure elements—such as community gardens, bio-swales, permeable surfaces and tree planting—requires understanding complex spatial relationships: topography, water flow patterns, existing structures, population density and community access points.

Interactive Map: Potential Green Space Locations

Code 
library(leaflet)
library(dplyr)

# Simulate potential green space locations in a slum area
set.seed(42)
n_sites <- 50

green_sites <- data.frame(
  lat = runif(n_sites, -1.3, -1.25),
  lng = runif(n_sites, 36.8, 36.85),
  priority = sample(c("High", "Medium", "Low"), n_sites, replace = TRUE,
                   prob = c(0.3, 0.4, 0.3)),
  type = sample(c("Community Garden", "Bio-swale", "Tree Planting", "Green Roof"),
               n_sites, replace = TRUE),
  suitability_score = round(runif(n_sites, 50, 100), 1)
)

# Color palette
pal <- colorFactor(
  palette = c("darkgreen", "orange", "red"),
  domain = c("High", "Medium", "Low")
)

# Create interactive map
leaflet(green_sites) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(
    lng = ~lng,
    lat = ~lat,
    color = ~pal(priority),
    fillOpacity = 0.7,
    radius = ~suitability_score/10,
    popup = ~paste(
      "<strong>Type:</strong>", type, "<br>",
      "<strong>Priority:</strong>", priority, "<br>",
      "<strong>Suitability Score:</strong>", suitability_score
    ),
    label = ~type
  ) %>%
  addLegend(
    position = "bottomright",
    pal = pal,
    values = ~priority,
    title = "Priority Level"
  )

Interactive map showing potential locations for green infrastructure

Real-World Impact

In Mumbai’s Dharavi settlement, researchers used R to analyze drainage patterns and identify 47 optimal locations for constructed wetlands that could treat greywater while creating community green spaces. The data-driven approach increased stakeholder buy-in and secured funding from multiple NGOs.

Use Case 2: Climate Vulnerability Assessment and Adaptive Design

The Challenge

Slum areas are disproportionately vulnerable to climate impacts: flooding due to poor drainage, heat stress from lack of vegetation and dense construction and water scarcity during droughts. Green architecture must be designed to address these specific vulnerabilities.

Interactive Visualization: Green Roof Cooling Impact

Code 
library(ggplot2)
library(plotly)

# Model cooling impact of green roofs
green_roof_coverage <- seq(0, 100, by = 5)  # Percentage coverage
temperature_reduction <- 0.05 * green_roof_coverage  # Empirical model
heat_days_avoided <- round(temperature_reduction * 45 / 2)  # Based on 45 heat days/year

cooling_model <- data.frame(
  coverage = green_roof_coverage,
  temp_reduction = temperature_reduction,
  heat_days_avoided = heat_days_avoided,
  cost = green_roof_coverage * 800,  # $800 per % coverage
  cost_per_degree = (green_roof_coverage * 800) / (temperature_reduction + 0.01)
)

# Create interactive plot
p1 <- ggplot(cooling_model, aes(x = coverage, y = temp_reduction)) +
  geom_line(color = "#1a5e4a", size = 1.5) +
  geom_point(size = 3, alpha = 0.6,
            aes(text = paste("Coverage:", coverage, "%\n",
                           "Temp Reduction:", round(temp_reduction, 2), "°C\n",
                           "Heat Days Avoided:", heat_days_avoided))) +
  labs(title = "Temperature Reduction from Green Roof Implementation",
       x = "Green Roof Coverage (%)",
       y = "Average Temperature Reduction (°C)") +
  theme_minimal() +
  theme(plot.title = element_text(size = 14, face = "bold"))

ggplotly(p1, tooltip = "text")

Projected temperature reduction from green roof implementation

Cost-Effectiveness Analysis

Code 
p2 <- ggplot(cooling_model %>% filter(coverage > 0),
            aes(x = coverage, y = cost_per_degree)) +
  geom_line(color = "#2c7a64", size = 1.5) +
  geom_point(size = 3, alpha = 0.6,
            aes(text = paste("Coverage:", coverage, "%\n",
                           "Cost per °C:", scales::dollar(cost_per_degree)))) +
  geom_hline(yintercept = min(cooling_model$cost_per_degree[cooling_model$coverage > 20]),
            linetype = "dashed", color = "red") +
  labs(title = "Cost-Effectiveness of Green Roof Cooling",
       x = "Green Roof Coverage (%)",
       y = "Cost per Degree Celsius ($)") +
  theme_minimal()

ggplotly(p2, tooltip = "text")

Cost per degree of cooling achieved

Climate Justice

Green architecture in slum areas isn’t just about environmental sustainability—it’s about climate justice. Communities with the least resources often face the greatest climate risks. R enables evidence-based advocacy for interventions that protect vulnerable populations.

Use Case 3: Water Resource Management and Rainwater Harvesting

The Challenge

Many slum areas face water scarcity while simultaneously experiencing flooding during rainy seasons. Optimizing rainwater harvesting systems requires understanding rainfall patterns, calculating catchment potential and sizing storage infrastructure appropriately.

Interactive Analysis: Rainwater Harvesting Potential

Code 
library(lubridate)
library(tidyr)

# Simulate 3 years of daily rainfall data
set.seed(123)
dates <- seq.Date(from = as.Date("2022-01-01"),
                 to = as.Date("2024-12-31"),
                 by = "day")

# Create seasonal rainfall pattern (bimodal for East Africa)
day_of_year <- yday(dates)
base_rainfall <- 50 * (sin((day_of_year - 60) * 2 * pi / 365) +
                      sin((day_of_year - 290) * 2 * pi / 365) + 2)
rainfall_mm <- pmax(0, base_rainfall + rnorm(length(dates), 0, 15))

rainfall_data <- data.frame(
  date = dates,
  rainfall_mm = rainfall_mm,
  month = month(dates, label = TRUE),
  year = year(dates)
)

# Calculate harvesting potential
roof_area <- 15000  # Total catchment area in m²
runoff_coefficient <- 0.8  # For corrugated metal roofs

rainfall_data <- rainfall_data %>%
  mutate(
    harvestable_volume = rainfall_mm / 1000 * roof_area * runoff_coefficient,
    households_served = harvestable_volume / 150  # 150L per HH per day
  )

# Monthly statistics
monthly_stats <- rainfall_data %>%
  group_by(month) %>%
  summarize(
    mean_rainfall = mean(rainfall_mm),
    total_volume = sum(harvestable_volume),
    avg_households = mean(households_served)
  )

# Create interactive visualization
p3 <- ggplot(monthly_stats, aes(x = month, y = mean_rainfall)) +
  geom_col(fill = "#1a5e4a", alpha = 0.7,
          aes(text = paste("Month:", month, "\n",
                          "Avg Rainfall:", round(mean_rainfall, 1), "mm\n",
                          "Water Harvested:", round(total_volume), "m³\n",
                          "Households Served:", round(avg_households)))) +
  labs(title = "Monthly Rainfall and Water Harvesting Potential",
       x = "Month",
       y = "Average Rainfall (mm)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggplotly(p3, tooltip = "text")

Monthly rainfall patterns and harvesting potential

Storage Optimization

Code 
# Optimize storage tank sizing
storage_sizes <- seq(50, 500, by = 25)  # m³
construction_costs <- storage_sizes * 250  # $250 per m³

# Simulate reliability for each storage size
reliability_analysis <- sapply(storage_sizes, function(size) {
  daily_need <- 150 * 30  # 30 households

  simulation <- rainfall_data %>%
    mutate(
      available = pmin(harvestable_volume, size),
      meets_need = available >= daily_need
    )

  reliability <- mean(simulation$meets_need) * 100
  return(reliability)
})

optimization_results <- data.frame(
  storage_size = storage_sizes,
  cost = construction_costs,
  reliability = reliability_analysis,
  cost_per_reliability = construction_costs / (reliability_analysis + 0.01)
)

# Find optimal size
optimal <- optimization_results %>%
  filter(reliability > 60) %>%
  arrange(cost_per_reliability) %>%
  slice(1)

p4 <- ggplot(optimization_results, aes(x = storage_size, y = reliability)) +
  geom_line(color = "#2c7a64", size = 1.5) +
  geom_point(aes(text = paste("Storage:", storage_size, "m³\n",
                             "Cost:", scales::dollar(cost), "\n",
                             "Reliability:", round(reliability, 1), "%"))) +
  geom_point(data = optimal, color = "red", size = 5, shape = 18) +
  geom_hline(yintercept = 60, linetype = "dashed", alpha = 0.5) +
  annotate("text", x = optimal$storage_size, y = optimal$reliability + 5,
          label = "Optimal Size", color = "red") +
  labs(title = "Storage Tank Size vs. Water Supply Reliability",
       x = "Storage Tank Size (m³)",
       y = "Supply Reliability (%)") +
  theme_minimal()

ggplotly(p4, tooltip = "text")

Optimal storage tank sizing analysis

Use Case 4: Air Quality Impact Assessment

The Challenge

Slum areas often face poor air quality due to proximity to industrial zones, heavy traffic, or reliance on solid fuels for cooking. Strategic placement of vegetation can improve air quality, but requires understanding the impact.

Interactive Visualization: Vegetation Impact on Air Quality

Code 
# Simulate air quality data with vegetation impact
set.seed(456)
n <- 200

vegetation_data <- data.frame(
  site_id = 1:n,
  ndvi = runif(n, 0.15, 0.75),  # Vegetation index
  distance_road = runif(n, 10, 500),  # Distance to main road (m)
  wind_speed = rnorm(n, 3, 1),
  season = sample(c("wet", "dry"), n, replace = TRUE)
)

# Model PM2.5 based on vegetation and other factors
vegetation_data <- vegetation_data %>%
  mutate(
    base_pm25 = 80,
    road_effect = -0.08 * distance_road,
    vegetation_effect = -50 * ndvi,
    wind_effect = -5 * wind_speed,
    seasonal_effect = ifelse(season == "dry", 15, 0),
    pm25 = pmax(10, base_pm25 + road_effect + vegetation_effect +
                wind_effect + seasonal_effect + rnorm(n, 0, 10))
  )

# Visualize relationship
p5 <- ggplot(vegetation_data, aes(x = ndvi, y = pm25, color = distance_road)) +
  geom_point(alpha = 0.6, size = 3,
            aes(text = paste("NDVI:", round(ndvi, 2), "\n",
                           "PM2.5:", round(pm25, 1), "µg/m³\n",
                           "Distance to Road:", round(distance_road), "m"))) +
  geom_smooth(method = "lm", se = FALSE, color = "#1a5e4a", size = 1.5) +
  scale_color_gradient(low = "red", high = "blue",
                      name = "Distance to\nRoad (m)") +
  labs(title = "Impact of Vegetation on Air Quality",
       x = "Vegetation Index (NDVI)",
       y = "PM2.5 Concentration (µg/m³)") +
  theme_minimal()

ggplotly(p5, tooltip = "text")

Air quality improvement from vegetation coverage

Projected Health Benefits

Code 
# Calculate scenarios
scenarios <- data.frame(
  scenario = c("Current", "10% Increase", "25% Increase", "50% Increase"),
  vegetation_increase = c(0, 0.10, 0.25, 0.50),
  avg_ndvi = c(mean(vegetation_data$ndvi),
              mean(vegetation_data$ndvi) * 1.10,
              mean(vegetation_data$ndvi) * 1.25,
              mean(vegetation_data$ndvi) * 1.50)
)

scenarios <- scenarios %>%
  mutate(
    predicted_pm25 = 80 - 50 * avg_ndvi,
    reduction_from_current = ((predicted_pm25[1] - predicted_pm25) /
                              predicted_pm25[1]) * 100,
    health_cases_prevented = round(reduction_from_current * 15)  # Per 1000 people
  )

p6 <- plot_ly(scenarios, x = ~scenario, y = ~predicted_pm25, type = "bar",
             marker = list(color = c("#d62728", "#ff7f0e", "#2ca02c", "#1a5e4a")),
             text = ~paste("PM2.5:", round(predicted_pm25, 1), "µg/m³\n",
                          "Reduction:", round(reduction_from_current, 1), "%\n",
                          "Health Cases Prevented:", health_cases_prevented, "per 1000"),
             hoverinfo = "text") %>%
  layout(title = "Air Quality Improvement Scenarios",
        xaxis = list(title = "Vegetation Scenario"),
        yaxis = list(title = "PM2.5 Concentration (µg/m³)"),
        showlegend = FALSE)

p6

Air quality improvement scenarios

Multi-Benefit Approach

Green buffers in slum areas provide multiple benefits: improved air quality, noise reduction, enhanced aesthetics, community gathering spaces and potential for urban agriculture. R helps quantify these co-benefits to build stronger cases for investment.

Use Case 5: Community Health Impact Assessment

The Challenge

Green architecture interventions in slum areas should be evaluated not just on environmental metrics, but on health outcomes. This requires integrating environmental data with community health indicators.

Interactive Analysis: Green Space Access and Health

Code 
# Simulate community health survey data
set.seed(789)
n_households <- 500

health_data <- data.frame(
  household_id = 1:n_households,
  distance_to_green = rexp(n_households, 0.005),  # Exponential distribution
  age = sample(18:75, n_households, replace = TRUE),
  household_size = sample(2:8, n_households, replace = TRUE),
  cooking_fuel = sample(c("charcoal", "kerosene", "lpg", "electric"),
                       n_households, replace = TRUE,
                       prob = c(0.5, 0.3, 0.15, 0.05)),
  housing_quality = sample(1:5, n_households, replace = TRUE)
)

# Generate health outcomes based on green access
health_data <- health_data %>%
  mutate(
    green_access = case_when(
      distance_to_green < 100 ~ "High",
      distance_to_green < 300 ~ "Medium",
      TRUE ~ "Low"
    ),
    green_access = factor(green_access, levels = c("Low", "Medium", "High")),
    # Risk factors for respiratory illness
    base_risk = 0.15,
    distance_risk = distance_to_green * 0.0003,
    fuel_risk = case_when(
      cooking_fuel == "charcoal" ~ 0.15,
      cooking_fuel == "kerosene" ~ 0.10,
      cooking_fuel == "lpg" ~ 0.03,
      TRUE ~ 0
    ),
    age_risk = ifelse(age > 50, 0.08, 0),
    total_risk = pmin(0.8, base_risk + distance_risk + fuel_risk + age_risk),
    respiratory_illness = rbinom(n_households, 1, total_risk)
  )

# Calculate prevalence by green access
prevalence_summary <- health_data %>%
  group_by(green_access) %>%
  summarize(
    n = n(),
    cases = sum(respiratory_illness),
    prevalence = mean(respiratory_illness) * 100,
    se = sqrt(prevalence/100 * (1 - prevalence/100) / n) * 100
  )

# Visualize
p7 <- plot_ly(prevalence_summary, x = ~green_access, y = ~prevalence, type = "bar",
             marker = list(color = c("#d62728", "#ff7f0e", "#2ca02c")),
             error_y = list(type = "data", array = ~se),
             text = ~paste("Access Level:", green_access, "\n",
                          "Prevalence:", round(prevalence, 1), "%\n",
                          "Cases:", cases, "out of", n),
             hoverinfo = "text") %>%
  layout(title = "Respiratory Illness Prevalence by Green Space Access",
        xaxis = list(title = "Green Space Access Level"),
        yaxis = list(title = "Prevalence (%)"),
        showlegend = FALSE)

p7

Respiratory illness prevalence by green space access

Impact Estimation

Code 
# Calculate potential impact of intervention
current_low <- health_data %>% filter(green_access == "Low")
high_access_rate <- mean(health_data$respiratory_illness[health_data$green_access == "High"])

impact_summary <- data.frame(
  metric = c("Current Cases (Low Access)",
            "Expected Cases After Intervention",
            "Cases Prevented",
            "Reduction (%)"),
  value = c(
    sum(current_low$respiratory_illness),
    round(nrow(current_low) * high_access_rate),
    sum(current_low$respiratory_illness) - round(nrow(current_low) * high_access_rate),
    round((1 - high_access_rate / mean(current_low$respiratory_illness)) * 100, 1)
  )
)

knitr::kable(impact_summary, col.names = c("Impact Metric", "Value"),
            caption = "Estimated Health Impact of Green Space Intervention")
Estimated Health Impact of Green Space Intervention
Impact Metric Value
Current Cases (Low Access) 55.0
Expected Cases After Intervention 34.0
Cases Prevented 21.0
Reduction (%) 38.2

Use Case 6: Resource Optimization and Cost-Benefit Analysis

The Challenge

Development organizations and municipalities face budget constraints. Green architecture interventions must demonstrate clear value for money while balancing multiple objectives: environmental impact, social benefits and financial feasibility.

Interactive Optimization: Intervention Portfolio

Code 
# Define potential interventions
interventions <- data.frame(
  intervention = c("Green Roofs", "Community Gardens", "Tree Planting",
                   "Bio-swales", "Permeable Pavement", "Vertical Gardens"),
  cost_per_unit = c(80, 25, 50, 120, 60, 40),
  carbon_benefit = c(15, 8, 21, 5, 3, 10),
  cooling_benefit = c(20, 5, 15, 8, 10, 12),
  water_benefit = c(25, 10, 5, 30, 35, 8),
  social_benefit = c(10, 30, 15, 5, 8, 20)
)

# Calculate total benefits and cost-effectiveness
interventions <- interventions %>%
  mutate(
    total_benefit = carbon_benefit + cooling_benefit + water_benefit + social_benefit,
    cost_effectiveness = total_benefit / cost_per_unit
  ) %>%
  arrange(desc(cost_effectiveness))

# Create interactive grouped bar chart for multi-benefit comparison
library(tidyr)

benefits_long <- interventions %>%
  select(intervention, carbon_benefit, cooling_benefit, water_benefit, social_benefit) %>%
  pivot_longer(cols = -intervention, names_to = "benefit_type", values_to = "score") %>%
  mutate(
    benefit_type = case_when(
      benefit_type == "carbon_benefit" ~ "Carbon",
      benefit_type == "cooling_benefit" ~ "Cooling",
      benefit_type == "water_benefit" ~ "Water",
      benefit_type == "social_benefit" ~ "Social"
    )
  )

p8 <- plot_ly(benefits_long, x = ~benefit_type, y = ~score, color = ~intervention,
             type = "bar",
             colors = c("#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02"),
             text = ~paste(intervention, "\n",
                          benefit_type, "Benefit:", score),
             hoverinfo = "text") %>%
  layout(title = "Multi-Benefit Comparison of Green Interventions",
        xaxis = list(title = "Benefit Type"),
        yaxis = list(title = "Benefit Score"),
        barmode = "group",
        legend = list(title = list(text = "Intervention")))

p8

Multi-benefit comparison of green interventions

Budget Sensitivity Analysis

Code 
# Simulate optimal allocation at different budgets
budget_range <- seq(10000, 100000, by = 5000)

# Simplified optimization: select interventions by cost-effectiveness
get_optimal_benefit <- function(budget) {
  available_budget <- budget
  total_benefit <- 0

  for(i in 1:nrow(interventions)) {
    units <- floor(available_budget / interventions$cost_per_unit[i])
    if(units > 0) {
      benefit_added <- units * interventions$total_benefit[i]
      cost_spent <- units * interventions$cost_per_unit[i]

      total_benefit <- total_benefit + benefit_added
      available_budget <- available_budget - cost_spent
    }
  }

  return(total_benefit)
}

sensitivity_results <- data.frame(
  budget = budget_range,
  total_benefit = sapply(budget_range, get_optimal_benefit)
) %>%
  mutate(
    benefit_per_dollar = total_benefit / budget,
    marginal_benefit = c(0, diff(total_benefit)) / c(1, diff(budget))
  )

p9 <- ggplot(sensitivity_results, aes(x = budget, y = total_benefit)) +
  geom_line(color = "#1a5e4a", size = 1.5) +
  geom_point(size = 2, alpha = 0.6,
            aes(text = paste("Budget:", scales::dollar(budget), "\n",
                           "Total Benefit:", round(total_benefit), "\n",
                           "Benefit per $:", round(benefit_per_dollar, 2)))) +
  labs(title = "Budget Sensitivity Analysis",
       x = "Budget ($)",
       y = "Total Benefit Score") +
  scale_x_continuous(labels = scales::dollar) +
  theme_minimal()

ggplotly(p9, tooltip = "text")

Total benefits achievable at different budget levels

Use Case 7: Monitoring and Evaluation with Sensor Networks

The Challenge

After implementing green architecture, tracking performance and impact is essential for adaptive management and demonstrating success to funders and stakeholders.

Interactive Time Series: Temperature Monitoring

Code 
# Simulate sensor data
set.seed(321)
dates_sensor <- seq.POSIXt(
  from = as.POSIXct("2024-01-01"),
  to = as.POSIXct("2024-12-31"),
  by = "hour"
)

intervention_date <- as.POSIXct("2024-06-01")

sensor_data <- data.frame(
  timestamp = dates_sensor
) %>%
  mutate(
    hour = hour(timestamp),
    day_of_year = yday(timestamp),
    is_post_intervention = timestamp >= intervention_date,

    # Base temperature pattern (daily and seasonal)
    base_temp = 25 + 5 * sin((day_of_year - 80) * 2 * pi / 365) +
                8 * sin((hour - 6) * 2 * pi / 24),

    # Intervention effect (cooling)
    intervention_effect = ifelse(is_post_intervention, -2.5, 0),

    # Add noise
    temperature = base_temp + intervention_effect + rnorm(length(dates_sensor), 0, 1.5)
  )

# Calculate daily averages for cleaner visualization
daily_temps <- sensor_data %>%
  mutate(date = as.Date(timestamp)) %>%
  group_by(date) %>%
  summarize(
    avg_temp = mean(temperature),
    max_temp = max(temperature),
    min_temp = min(temperature),
    is_post = first(is_post_intervention)
  )

# Calculate statistics
pre_intervention_avg <- mean(daily_temps$avg_temp[!daily_temps$is_post])
post_intervention_avg <- mean(daily_temps$avg_temp[daily_temps$is_post])
temp_reduction <- pre_intervention_avg - post_intervention_avg

p10 <- plot_ly(daily_temps, x = ~date, y = ~avg_temp, type = "scatter", mode = "lines",
              line = list(color = "#2c7a64", width = 2),
              name = "Average Temperature",
              text = ~paste("Date:", date, "\n",
                           "Avg Temp:", round(avg_temp, 1), "°C"),
              hoverinfo = "text") %>%
  add_trace(y = ~max_temp, name = "Max Temp",
           line = list(color = "#d62728", dash = "dash", width = 1),
           showlegend = TRUE) %>%
  add_trace(y = ~min_temp, name = "Min Temp",
           line = list(color = "#1f77b4", dash = "dash", width = 1),
           showlegend = TRUE) %>%
  add_segments(x = intervention_date, xend = intervention_date,
              y = min(daily_temps$min_temp), yend = max(daily_temps$max_temp),
              line = list(color = "red", dash = "dot", width = 2),
              name = "Intervention Date") %>%
  layout(title = "Temperature Monitoring: Before and After Green Infrastructure",
        xaxis = list(title = "Date"),
        yaxis = list(title = "Temperature (°C)"),
        hovermode = "x unified",
        annotations = list(
          x = intervention_date,
          y = max(daily_temps$max_temp),
          text = "Green Infrastructure<br>Installed",
          showarrow = TRUE,
          arrowhead = 2,
          arrowcolor = "red",
          ax = 50,
          ay = -40
        ))

p10

Temperature monitoring before and after green infrastructure

Impact Summary

Code 
# Statistical test
pre_temps <- daily_temps$avg_temp[!daily_temps$is_post]
post_temps <- daily_temps$avg_temp[daily_temps$is_post]
t_test <- t.test(pre_temps, post_temps)

impact_results <- data.frame(
  Metric = c(
    "Pre-Intervention Average (°C)",
    "Post-Intervention Average (°C)",
    "Temperature Reduction (°C)",
    "Reduction (%)",
    "Statistical Significance (p-value)",
    "Heat Days Prevented (>32°C)"
  ),
  Value = c(
    round(pre_intervention_avg, 2),
    round(post_intervention_avg, 2),
    round(temp_reduction, 2),
    round(temp_reduction / pre_intervention_avg * 100, 1),
    format(t_test$p.value, scientific = TRUE, digits = 3),
    sum(daily_temps$max_temp[!daily_temps$is_post] > 32) -
      sum(daily_temps$max_temp[daily_temps$is_post] > 32)
  )
)

knitr::kable(impact_results,
            caption = "Temperature Reduction Impact Assessment")
Temperature Reduction Impact Assessment
Metric Value
Pre-Intervention Average (°C) 24.73
Post-Intervention Average (°C) 22.65
Temperature Reduction (°C) 2.08
Reduction (%) 8.4
Statistical Significance (p-value) 4.67e-08
Heat Days Prevented (>32°C) -6

Integrating R into Green Architecture Workflows

Successfully leveraging R for green architecture in slum areas requires thoughtful integration into existing workflows:

1. Data Collection Infrastructure

  • Deploy low-cost sensors: Arduino and Raspberry Pi-based environmental monitoring
  • Mobile data collection: Use R packages like shiny and rdrop2 for field surveys
  • Open data integration: Access satellite imagery (Landsat, Sentinel), OpenStreetMap, government datasets

2. Collaborative Analysis Platforms

  • R Markdown reports: Create reproducible analysis documents combining code, visualizations and narrative
  • Shiny dashboards: Build interactive tools for stakeholders without R expertise
  • Version control: Use GitHub to track analysis evolution and enable team collaboration

3. Capacity Building

  • Train local teams: Develop R skills within local organizations for sustainable capacity
  • Document workflows: Create clear, commented code and tutorials
  • Share resources: Contribute to open-source packages specific to urban development

4. Ethical Considerations

  • Data privacy: Protect community member identities in health and survey data
  • Equitable benefit sharing: Ensure data insights benefit the communities studied
  • Community consent: Engage communities in data collection and use decisions
  • Avoid extractive research: Partner with, rather than study, communities

Conclusion: R as a Tool for Environmental Justice

Green architecture in slum areas represents one of the most critical challenges of the 21st century. As climate change intensifies and urban populations grow, we must find ways to create sustainable, resilient, healthy living environments for the world’s most vulnerable communities.

R provides the analytical foundation for this work: enabling evidence-based design, optimizing limited resources, demonstrating impact to funders and—perhaps most importantly—empowering communities with data to advocate for their own needs.

The use cases explored in this article represent just the beginning. As R continues to evolve, with growing capabilities in machine learning, real-time data processing and cloud computing, its potential to support transformative green architecture in informal settlements will only expand.

For architects, urban planners, development organizations and data scientists, the call is clear: leverage R’s power not just for analysis, but for justice. Use data not just to understand slum areas, but to transform them. Apply statistical rigor not just for academic publication, but for community empowerment.

The future of sustainable urban development will be built on data. With R, we can ensure that future includes everyone.

Getting Started with R for Green Architecture

Ready to apply R to your green architecture projects? Here are resources to begin:

Essential R Packages

  • Spatial analysis: sf, terra, raster, leaflet, tmap
  • Environmental data: openair, climatol, nasapower
  • Optimization: lpSolve, GA, optimization
  • Visualization: ggplot2, plotly, shiny
  • Reporting: rmarkdown, quarto, officer

Learning Resources

Data Sources

About Kwiz Computing Technologies

We specialize in helping organizations leverage R for production software development, particularly in data-intensive domains. Our expertise includes enterprise-grade Shiny application development using the Rhino framework, R package development and analytical systems that go from prototype to production seamlessly.

Interested in exploring whether R makes sense for your organization? Contact us for a no-obligation consultation where we can discuss your specific challenges and whether R might be part of the solution.