SERO (Spatial Emergency Response Optimization) is an R package that provides comprehensive tools for optimizing emergency service locations using spatial statistical methods. The package combines hotspot analysis, kernel density estimation, and spatial optimization algorithms to help emergency service planners make data-driven decisions about station placement and resource allocation.
🚨 Perfect for: Emergency service planners, urban planners, researchers, and policy makers working with spatial emergency response optimization.
- Hotspot Detection: Identify accident concentration areas using kernel density estimation
- Risk Surface Analysis: Generate smooth risk surfaces through kernel density heatmaps
- Optimal Location Finding: Calculate strategic emergency service locations using spatial optimization
- Route Analysis: Analyze emergency response routes with performance metrics
- Comprehensive Visualization: Professional-quality spatial visualizations for analysis and reporting
- Real-World Application: Demonstrated with traffic accident data from Münster, Germany
- Production Ready: Passes R CMD check with comprehensive testing and documentation
Install the latest version from GitHub:
# Install from GitHub
if (!require("devtools")) install.packages("devtools")
devtools::install_github("iprincegh/SERO")
# Load the package
library(SERO)- R (>= 4.0.0)
- Required packages: sf, ggplot2, dplyr, gridExtra
- Spatial data processing capabilities
Get started with SERO in just a few lines:
library(SERO)
# 1. Load the comprehensive spatial dataset
data <- sero_load_data()
# 2. Perform hotspot analysis for high-risk accidents
hotspots <- sero_hotspots(data$accident,
risk_categories = c(1, 2), # Fatal and serious accidents
buffer = 1000)
# 3. Generate risk surface heatmap
heatmap <- sero_heatmap(data$accident,
risk_categories = c(1, 2),
bandwidth = 1000,
data = data)
# 4. Find optimal emergency station locations
optimal_locations <- sero_find_optimal_locations(data,
num_locations = 5,
risk_categories = c(1, 2))
# 5. Analyze emergency response routes
sample_accidents <- data$accident[sample(nrow(data$accident), 10), ]
routes <- sero_routes(optimal_locations, sample_accidents, data = data)
# 6. Visualize results
plot(hotspots)
plot(heatmap)
plot(optimal_locations)
summary(routes)sero_load_data(): Load comprehensive spatial dataset with accidents, boundaries, roads, and land use
sero_hotspots(): Identify accident hotspots using kernel density estimationsero_heatmap(): Generate kernel density risk surface visualizations
sero_find_optimal_locations(): Calculate optimal emergency service locationssero_routes(): Analyze emergency response routes and performance
plot()methods for all analysis objects- Professional-quality maps with customizable styling
- Individual plot displays for detailed analysis
SERO uses a sophisticated spatial optimization algorithm with these principles:
- Distance-Based Accessibility: Calculates accessibility scores based on Euclidean distances from potential emergency stations to high-risk accident sites
- Weighted Risk Categories: Fatal accidents receive higher priority weights than serious accidents
- Coverage Optimization: Minimizes average distance while ensuring reasonable maximum response distances
- Iterative Placement: Adjusts station positions iteratively to minimize accessibility scores
- Spatial Constraints: Considers geographical constraints and practical feasibility
- Comprehensive Analysis: All accident severity levels (fatal, serious, light)
- High-Risk Focus: Fatal and serious accidents only
- Critical Zones: Fatal accidents exclusively
- Performance Comparison: Trade-offs between different approaches
The package includes a comprehensive vignette with real-world examples:
# View the main vignette
vignette("SERO-intro", package = "SERO")
# Browse all vignettes
browseVignettes("SERO")Vignette Contents:
- 📖 Introduction and motivation for data-driven emergency planning
- ⚙️ Detailed optimization algorithm explanation
- 📊 Comprehensive data exploration and accident analysis
- 🎯 Spatial pattern analysis with hotspot detection
- 📍 Emergency service optimization strategies
- 💡 Results discussion and strategic recommendations
- 🔍 Less than 800 words of explanatory text + full code examples
library(SERO)
data <- sero_load_data()
# Explore accident severity distribution
severity_counts <- table(data$accident$UKATEGORIE)
print(severity_counts)# Analyze different risk categories
hotspots_all <- sero_hotspots(data$accident,
risk_categories = c(1, 2, 3),
buffer = 1000)
hotspots_high_risk <- sero_hotspots(data$accident,
risk_categories = c(1, 2),
buffer = 1000)
hotspots_fatal <- sero_hotspots(data$accident,
risk_categories = c(1),
buffer = 800)
# Visualize results
plot(hotspots_high_risk)
summary(hotspots_high_risk)# Generate heatmaps for different severity levels
heatmap_fatal <- sero_heatmap(data$accident,
risk_categories = c(1),
bandwidth = 1000,
data = data,
color_scheme = "plasma",
basemap = "districts")
heatmap_combined <- sero_heatmap(data$accident,
risk_categories = c(1, 2),
bandwidth = 1200,
data = data,
color_scheme = "magma",
basemap = "districts")
plot(heatmap_fatal)
plot(heatmap_combined)# Compare different station configurations
optimal_3 <- sero_find_optimal_locations(data, num_locations = 3, risk_categories = c(1, 2))
optimal_5 <- sero_find_optimal_locations(data, num_locations = 5, risk_categories = c(1, 2))
optimal_7 <- sero_find_optimal_locations(data, num_locations = 7, risk_categories = c(1, 2))
# Performance comparison
performance_data <- data.frame(
Stations = c(3, 5, 7),
Avg_Coverage = c(mean(optimal_3$accessibility_score),
mean(optimal_5$accessibility_score),
mean(optimal_7$accessibility_score)),
Max_Distance = c(max(optimal_3$accessibility_score),
max(optimal_5$accessibility_score),
max(optimal_7$accessibility_score))
)
print(performance_data)# Analyze emergency response routes
high_risk_accidents <- data$accident[data$accident$UKATEGORIE %in% c(1, 2), ]
sample_accidents <- high_risk_accidents[sample(nrow(high_risk_accidents), 12), ]
routes <- sero_routes(optimal_5, sample_accidents, max_routes = 12, data = data)
# Performance metrics
summary(routes)
# Visualize routes
plot(routes)SERO works with spatial datasets containing:
- Accident Data: Point locations with severity categories
- Administrative Boundaries: District/area polygons
- Road Networks: LineString geometries
- Land Use: Polygon data for geographic context
- Population Data: Optional demographic information
All spatial data should be in a consistent coordinate reference system (CRS).
- ✅ Production Ready: Package passes R CMD check with minimal warnings
- 📝 Optimized Vignette: Condensed documentation to <800 words while maintaining full functionality
- 🧹 Clean Codebase: Removed unnecessary files and improved .gitignore
- 🎨 Enhanced Visualizations: Individual plot displays for better analysis clarity
- 📊 Comprehensive Testing: Full test suite ensuring reliability
- 🌍 Real Data Application: Validated with Münster traffic accident dataset
SERO is ideal for:
- 🚑 Emergency Service Planning: Optimize ambulance, fire station, and police locations
- 🏙️ Urban Planning: Inform infrastructure development based on risk patterns
- 🚦 Traffic Safety: Identify high-risk areas for targeted interventions
- 💰 Resource Allocation: Balance coverage efficiency with budget constraints
- 📋 Policy Analysis: Evaluate emergency service deployment strategies
- 🎓 Academic Research: Spatial optimization and emergency response studies
- Bandwidth Selection: Affects hotspot sensitivity and heatmap smoothness
- Buffer Zones: Influence hotspot detection radius
- Risk Categories: Determine which accident types to prioritize
- Station Numbers: Trade-off between coverage and resource requirements
If you use SERO in your research, please cite:
SERO Development Team (2025). SERO: Spatial Emergency Response Optimization.
R package version 0.1.0. https://github.com/iprincegh/SERO
@Manual{sero2025,
title = {SERO: Spatial Emergency Response Optimization},
author = {Prince Oppong Boakye},
year = {2025},
note = {R package version 0.1.0},
url = {https://github.com/iprincegh/SERO},
}
We welcome contributions! 🤝
- Fork the repository
- Create a feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the GPL-3 License - see the LICENSE file for details.
- 🐛 Issues: GitHub Issues
- 💬 Discussions: GitHub Discussions
- 📧 Email: Available through GitHub profile
- 📖 Documentation: Full documentation in package vignettes
- 🙏 Traffic accident data courtesy of Münster, Germany
- ⭐ Built with the excellent R spatial ecosystem: sf, ggplot2, dplyr
- 📚 Inspired by spatial optimization research in emergency services
- 🌟 Special thanks to the R community for spatial analysis tools
Made with ❤️ for emergency service optimization and spatial analysis