1. Introduction | 2. Build routable transport network with build_network() | Increase Java memory and load libraries | 3. Accessibility: quick and easy approach | 4. Accessibility: flexible approach | 5. Map Accessibility | 5.1 Choropleth maps | 5.2 Spatial interpolation | Cleaning up after usage | References

1. Introduction | 1.1 Details | 2. Reprex: the public transport system of Porto Alegre | 3. Setting up the fare structure | 3.1 Global Properties | max_discounted_transfers | transfer_time_allowance | fare_cap | 3.2 Configure fares by transport mode | 3.3 Configure fares by transfers | 3.4 Routes configuration | 4. Calculating travel time and accessibility accounting for monetary costs | 4.1 Travel time with monetary cost | 4.2 Calculating accessibility with monetary cost | Cleaning up after usage

1. Why do some trips from/to the same ID have travel times larger than zero? | 2. Is it possible to run r5r with custom modifications to street network data? | 3. Why are the output results of travel_time_matrix() and detailed_itineraries() different? | 4. What does the ERROR "Geographic extent of street layer exceeds limit" mean? and what to do about it? | 5. Is it possible to use custom car speed data with r5r? | 6. Why do I get identical results by public transport and walking?

1. Introduction | 2. Installation | 3. Usage | 3.1 Data requirements: | 4. Demonstration on sample data | Data | 4.1 Building routable transport network with build_network() | 4.2 Accessibility analysis | 4.3 Routing analysis | Fast many to many travel time matrix | Expanded travel time matrix with minute-by-minute estimates | Detailed itineraries | Visualize results | Cleaning up after usage

1. Introduction | 2. Build routable transport network with build_network() | Increase Java memory and load libraries | 3. Calculating and visualizing isochrones | 3.1 Polygon-based isochrones | 3.2 Line-based isochrones | Cleaning up after usage

1. Introduction | 2. What the pareto_frontier means. | 3. Demonstration of pareto_frontier(). | 3.1 Build routable transport network with build_network() | 3.2 Set up the fare structure | 3.3 Calculating a pareto_frontier(). | Cleaning up after usage | References

1. Introduction | 2. Build routable transport network with build_network() | 3. The travel_time_matrix() function | 4. The expanded_travel_time_matrix() function | 5. The arrival_travel_time_matrix() function | Cleaning up after usage | References

1. Introduction | 2. Build routable transport network with build_network() | 3. Detailed info by trip segment for multiple trip alternatives | 3.1 Visualize results | 4. Other options | 5. Hack for frequency-based GTFS feeds | Cleaning up after usage

1. Introduction | 2. Changing car speeds | 2.1 Changing car speeds by OSM edge | 2.1.1 Setting different congestion levels by road hierarchy | 2.1.2 Setting the same speed limit for the selected road types | Extra tip: | 2.2 Changing car speeds with a spatial polygon | 3. Changing cycling LTS values | 3.1 Changing LTS by OSM edge | 3.2. Changing LTS with spatial lines | Cleaning up after usage

1. Introduction | The problem | The solution | 2. How the time_window works and how to interpret the results. | 3. Demonstration of time_window. | 3.1 Build routable transport network with build_network() | 3.2 Accessibility with time_window. | 3.3 Travel time matrix with time_window. | 3.4 Expanded travel time matrix with time_window. | 3.5 Detailed itineraries with time_window. | Cleaning up after usage | References

TL;DR | Prepare data | Spatial join | Spatial filter | Spatial distances | Dissolving geometries | Geometry predicates | Export results

The inputs | Step 1: clean the pings | Step 2: extract trips and stop times | Step 3: check what was kept | Step 4: the shapes actually driven | Backends | Where next

Reading an archived feed | A worked example | Terminals and stops from a baseline feed | Inputs without a GTFS | Extracting trips and stop times | From inference tables to a valid GTFS feed | Shapes: the geometry actually driven | Notes for real archives

Setup Data | 1) Extensive vs. Intensive Interpolation | Case A: Extensive Variables (Counts) | Case B: Intensive Variables (Densities/Ratios) | Visual Comparison | 2) High Performance: Output as Tibble | 3) Database Mode: Large Data Workflows | Cleanup

Installation | Reading data | Processing data | Working in a database | Creating a connection | Non-persistent database | Persistent database | Reopening saved tables | Technical Note on Interoperability

1) In-memory: pass sf, return sf | 2) Connected: pass table names in DuckDB, return sf | 3) Write-to-DB: create a new table with the join result | Spatial Join Predicates: | Clean up

Create a temporary directory and copy example OSM PBF file into it | Start local OSRM server | Load spatial data | Calculate route between sampled points | Plot the route on top of the street network | Stop local OSRM server and cleanup

1. Spatial data with zoning boundaries | 1.1 Districts | 1.2 Municipalities | 1.3 LUAs (Large Urban Areas) | 2. Mobility data | 2.1. Origin-destination data | 2.2. Population by trip count data | 2.3. Population by overnight stay data | Advanced use

1. Spatial data with zoning boundaries | 1.1 Districts | 1.2 Municipalities | 2. Mobility data | 2.1. Origin-Destination data | 2.2. Population by trip count data | Advanced use

Introduction | Two ways to get the data | Analysing large datasets | How to choose between DuckDB, Parquet, and CSV | Analysis Speed | Convenience of data analysis | Scenarios of getting the data | Setup | Getting a single day with spod_get() | Analysing the data using DuckDB database | Convert to DuckDB | Load the converted DuckDB | Analysing the data using Parquet | Convert to Parquet | Load the converted Parquet | Download all available data | Download all data | Convert all data into analysis ready format | Conversion speed | Connecting to and analysing the converted datasets

Introduction | Setup | Get the data | Get all flows with at least 1000 trips | Get only trips of certain length | Get only trips between certain municipalities | All trips from Madrid | All trips from Madrid to Barcelona

1. Quickly Download and Install Java | Install rJavaEnv | Quick Install Java JDK | 2. List Available Java Versions | 3. Check Java installation | Note for Linux users | 4. Return things to their original state | Unset Java | Delete Java from the project directory | 5. Complete Cleanup

1. Set-up cache folder location | 2. List available Java versions | 3. Download Java JDK distributions | 4. Manage downloaded Java JDK distributions | 5. Install from downloaded Java JDK distributions into current project | 6. Manually set installed Java environment | 7. Cleanup

Introduction | Example: Create a 100m grid for Spain | Setup | Get the country boundary | Create grid directly to GeoPackage | Performance Note | Create grid directly to GeoTIFF (raster) | Raster Attribute Tables (RAT) | Format-Specific RAT Behavior | Parallel Generation | Note on Parallel Backends | Read the grid from disk | Controlling memory usage | Automatic Memory Detection | Output formats | Vector formats (output_type = "sf_polygons" or "sf_points") | Recommended Formats | Other Supported Formats | Format Support | For Text Output | Raster formats (output_type = "spatraster") | Performance Comparison | Text/tabular formats (output_type = "dataframe") | Tips for large grids

1. Install Java | 2. Set the environment for building rJava | 3. Install rJava from source | Order Matters: use_java() Before library(rJava)

The Critical Decision: Does Your Package Use rJava? | Scenario A: Packages Using Command-Line Java (No rJava) | Recommended Pattern: local_java_env() | Using with_java_env() | Performance: Using .use_cache | Controlling Whether to Use System Java | Scenario B: Packages Importing rJava | Pattern 1: The Guard (Recommended) | Pattern 2: Process Isolation (Advanced) | Choosing Between use_java and java_ensure | Strategy: "Inform and Guide" | 1. Add rJavaEnv to Imports | 2. Document the Requirement | 3. Optional: Provide a Setup Helper | Performance & Caching | When to use .use_cache = TRUE | Debugging | CI/CD (GitHub Actions)

Introduction | Installation | Example: Create a 1km grid for Luxembourg

How to use use_java() | How to use with targets and callr

Introduction | Installation | Setting the Cache Path | Extracting Documentation | Choosing what to dump to text | Handling Temporary Files

Example 1: Visualizing Basic Flows | Example 2: Flows with Date-Time | Example 3: Flows with Date Only | Integration with Shiny

Case 1: Create a Turing-style research compendium | Other ideas | Case 2: Enhance an existing research compendium | What to share?

General questions | Docker questions | Apptainer/Singularity questions