A comprehensive R package for analyzing Spanish mobility patterns using MITMA (Ministry of Transport, Mobility and Urban Agenda) data with advanced spatial analysis capabilities, automatic map generation, and best-practice spatial workflows.
- 🗺️ Automatic Spatial Visualization: All analysis functions create beautiful maps when geometries are provided
- 📊 Advanced Spatial Analysis: Buffer analysis, accessibility matrices, spatial lag, containment analysis
- 🤖 Machine Learning Integration: Predict mobility patterns with built-in ML models
- 🔍 Smart Filtering: Download only needed data by region, province, or specific zones
- ⚡ Memory Efficient: Optimized for large datasets with intelligent memory management
- 🛠️ Best Practices: Robust coordinate transformations, geometry validation, and error handling
devtools::install_github("iprincegh/mobspain-r-package")
library(mobspain)# Complete analysis in one function
results <- quick_analysis(dates = "2020-02-14", level = "dist")
print(results$maps$indicators) # View mobility map
# Regional analysis with buffer zones
madrid_data <- get_region_mobility("Madrid", dates = "2020-02-14")
buffer_analysis <- analyze_mobility_buffer(
center_points = c(-3.7038, 40.4168), # Madrid coordinates
buffer_km = 20,
dates = "2020-02-14"
)Retrieve mobility data between zones with advanced filtering
dates: Date(s) as string or vector ("2020-02-14" or c("2020-02-14", "2020-02-16"))level: Spatial level ("dist", "muni", "prov")max_rows: Maximum rows to return (default: 10000 for memory management)zones_filter: Specific zone IDs to filter byregion_filter: Region name or province code to filter by
Get administrative boundaries with enhanced spatial statistics
- Returns zones with: centroid coordinates, area, perimeter, compactness, bbox dimensions
- Includes robust coordinate transformation and geometry validation
level: Spatial level ("dist", "muni", "prov")year: Year for administrative boundaries (default: 2023)
Get both mobility data and zones for a region in one call
region: Region name or province codedates: Date(s) to get mobility data forlevel: Spatial levelmax_rows: Maximum rows to return
Calculate how much mobility stays within zones
- Returns containment ratios with automatic spatial mapping
mobility_data: Output fromget_mobility()spatial_zones: sf object with zone geometriescreate_map: Whether to create spatial map
NEW: Select zones within buffer distance of points
- Robust coordinate transformation to projected CRS
- Returns zones with buffer distance information
zones: sf object with zone geometriescenter_points: Points as coordinates or sf objectbuffer_km: Buffer distance in kilometerscrs_proj: Projected CRS for accurate distance calculations
NEW: Calculate spatial accessibility between zones
- Supports exponential, linear, and power decay functions
- Returns normalized accessibility indices
zones: sf object with zone geometriesdecay_function: "exponential", "linear", or "power"max_distance_km: Maximum distance for accessibility calculation
NEW: Calculate spatial lag for spatial autocorrelation analysis
- Supports contiguity and distance-based spatial weights
zones: sf object with zone geometriesvariable: Variable name for spatial lag calculationmethod: "contiguity" or "distance"k: Number of nearest neighbors (for distance method)
NEW: Complete buffer-based mobility analysis
- Analyzes mobility patterns within buffer zones
- Returns zones, mobility data, and analysis results
center_points: Center points for buffer analysisbuffer_km: Buffer distance in kilometersdates: Date(s) for analysis
Find unusual mobility patterns using statistical methods
mobility_data: Output fromget_mobility()threshold: Number of standard deviations for anomaly detectionspatial_zones: sf object with zone geometriescreate_map: Whether to create spatial map
predict_patterns(mobility_data, prediction_dates, model_type = "linear_regression", spatial_zones = NULL, create_map = TRUE)
Predict future mobility patterns using machine learning
mobility_data: Historical mobility dataprediction_dates: Date(s) to predictmodel_type: "linear_regression" or "random_forest"spatial_zones: sf object with zone geometriescreate_map: Whether to create spatial map
NEW: Advanced outlier detection with spatial context
- Uses multiple statistical methods for robust outlier detection
- Returns outlier zones with spatial visualization
Calculate comprehensive mobility indicators
- Returns total trips, connectivity indices, trip density, and more
mobility_data: Output fromget_mobility()spatial_zones: sf object with zone geometries
NEW: Calculate enhanced spatial statistics
- Includes trip density, connectivity ratios, and spatial metrics
- Integrates with spatial zones for comprehensive analysis
Create flow maps showing movement between zones
mobility_data: Output fromget_mobility()spatial_zones: sf object with zone geometriestop_flows: Number of top flows to display
Create spatial maps from analysis results
analysis_result: Results from analysis functionsspatial_zones: sf object with zone geometriesmap_type: "choropleth", "points", "flows", or "auto"title: Map title
create_dashboard(mobility_data, spatial_zones, analyses = c("indicators", "containment", "anomalies"))
NEW: Create comprehensive analysis dashboard
- Combines multiple analyses into a single visualization
analyses: Vector of analyses to include in dashboard
Complete spatial analysis workflow
dates: Date(s) to analyzelevel: Spatial level
analyze_complete(mobility_data, spatial_zones, analyses = c("containment", "anomalies", "indicators"))
NEW: Comprehensive analysis with multiple metrics
- Runs multiple analyses and creates combined results
analyses: Vector of analyses to perform
analyze_spatial(mobility_data, spatial_zones, analyses = c("containment", "anomalies", "indicators"))
NEW: Spatial-focused analysis workflow
- Emphasizes spatial relationships and patterns
# Get mobility data and zones
mobility <- get_mobility(dates = "2020-02-14", level = "dist")
zones <- get_zones(level = "dist")
# Quick analysis with automatic mapping
indicators <- calc_indicators(mobility, zones)
print(indicators$map)# Get regional data
madrid_data <- get_region_mobility("Madrid", dates = "2020-02-14")
# Analyze containment
containment <- calculate_containment(madrid_data$mobility, madrid_data$zones)
print(containment$map)# Buffer analysis around Madrid
madrid_center <- c(-3.7038, 40.4168)
buffer_zones <- get_zones_buffer(zones, madrid_center, buffer_km = 20)
# Calculate accessibility matrix
accessibility <- calculate_accessibility_matrix(buffer_zones, max_distance_km = 30)
# Spatial lag analysis
spatial_lag <- calculate_spatial_lag(buffer_zones, "total_trips", method = "contiguity")# Get historical data
mobility_hist <- get_mobility(dates = c("2020-02-10", "2020-02-14"), level = "dist")
# Predict future patterns
predictions <- predict_patterns(
mobility_hist,
prediction_dates = "2020-02-15",
model_type = "random_forest",
spatial_zones = zones
)
print(predictions$spatial_map)# Filter by region name
madrid_mobility <- get_mobility(dates = "2020-02-14", region_filter = "Madrid")
# Filter by province code
valencia_mobility <- get_mobility(dates = "2020-02-14", region_filter = "46")
# Filter by specific zones
specific_zones <- get_mobility(dates = "2020-02-14", zones_filter = c("28079", "08019"))# Create comprehensive analysis dashboard
dashboard <- create_dashboard(
mobility_data = mobility,
spatial_zones = zones,
analyses = c("indicators", "containment", "anomalies")
)
print(dashboard$combined_map)# Optional configuration for caching and performance
configure_mobspain(
cache_dir = tempdir(),
max_cache_size = 500, # MB
parallel = FALSE,
cache_max_age_days = 7
)
# Check package status
mobspain_status()# Validate MITMA data structure
validate_mitma_data(mobility_data)
# Check Spanish holidays
check_spanish_holidays(dates = "2020-02-14")
# Comprehensive Spanish mobility data validation
validate_spanish_mobility_data(mobility_data)The package implements spatial analysis best practices:
- Coordinate Transformation: Automatic CRS handling with proper projections
- Geometry Validation: Robust geometry checking and repair
- Memory Management: Efficient processing of large datasets
- Error Handling: Comprehensive error checking and user feedback
- Spatial Statistics: Enhanced spatial metrics and indicators
# Analyze mobility within buffer zones
buffer_analysis <- analyze_mobility_buffer(
center_points = madrid_center,
buffer_km = 25,
dates = "2020-02-14",
level = "dist"
)# Calculate spatial accessibility
accessibility_matrix <- calculate_accessibility_matrix(
zones = madrid_zones,
decay_function = "exponential",
max_distance_km = 50
)# Calculate spatial lag for autocorrelation analysis
spatial_lag <- calculate_spatial_lag(
zones = madrid_zones,
variable = "total_trips",
method = "distance",
k = 8
)- Memory Efficient: Optimized for large datasets with intelligent sampling
- Parallel Processing: Optional parallel processing for large analyses
- Caching: Smart caching system to avoid redundant downloads
- Filtering: Download only needed data to reduce memory usage
Contributions are welcome! Please feel free to submit a Pull Request.
MIT License
Note: This package uses MITMA (Ministry of Transport, Mobility and Urban Agenda) data. Please ensure compliance with data usage policies when using this package for research or commercial purposes.