Filtering by agency_id, route_id, service_id, shape_id, stop_id, trip_id or route_type: | Filtering by day of the week or time of the day: | Filtering using a spatial extent

Mapping the transit network | Mapping routes by speed | Mapping stops by frequency

Setting route frequencies | Setting trip speeds | Converting stop times to frequencies | Interpolating stop times

Trips per day | Overlap periods

GTFS feeds | Basic usage | Read feeds | Analyse feeds | Manipulate feeds | Write feeds

Frequencies and timetables | Trip lengths, durations and speeds | Summarising a feed

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

Data Dictionary | Questionnaires | Interview manual

Installation | Basic usage | Larger-than-memory Data | Reproducible examples | Using Population data: | Using household data: | Sewage coverage: | Spatial distribution of rents: | Data cache

Why the 2022 census is different | Step 1. Get the data from IBGE | Step 2. Import it | Keep the original zip file

Data structure | Dictionary of variables | Reproducible examples | Example 1: | Example 2: Spatial distribution of income

Installation | General usage | Available data sets | Basic syntax | Important note about data resolution | Plot the data | Lazy evaluation with DuckDB and Arrow | Thematic maps | Merge external data | Plot thematic map | Using geobr together with censobr

Instalação | Utilização | Padronização de múltiplos campos simultaneamente | Padronização de campos individuais | Controle de verbosidade

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 accessibiilty 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 nework data? | 3. Why are the output results of time_travel_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 | 3.1 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 | 4. 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. A few options: | 4.1 Combining orings and destinations | 4.2 Keep geometry data in the output | 5. Hack for frequency-based GTFS feeds | 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

Demonstration on sample data | Palma Ratio | Gini Index | Concentration Index | Theil T | Foster-Greer-Thorbecke (FGT) poverty measures | References

Demonstration on sample data | Palma Ratio | Gini Index | Concentration Index | Theil T | Foster-Greer-Thorbecke (FGT) poverty measures | References

1. Introduction | 2. Simple or detailed fleet data table | Example of simple fleet table | Example of detailed fleet table | 3. Fleet characteristics vary by emission factor model | 4. Examples of fleet data tables | 4.1 Brazil: Environmental Company of Sao Paulo (CETESB): | 4.2 Europe: EMEP - European Environment Agency (EEA) | 4.3 United States: EMFAC2017 - California Air Resources Board (CARB) | 4.4 United States: MOVES3 - Environmental Protection Agency (EPA) | Learn more | Report a bug

1. Introduction | 2. Simple or detailed fleet data table | Example of simple fleet table | Example of detailed fleet table | 3. Fleet characteristics vary by emission factor model | 4. Examples of fleet data tables | 4.1 Brazil: Environmental Company of Sao Paulo (CETESB): | 4.2 Europe: EMEP - European Environment Agency (EEA) | 4.3 United States: EMFAC2017 - California Air Resources Board (CARB) | 4.4 United States: MOVES3 - Environmental Protection Agency (EPA) | Learn more | Report a bug

Download accessibility data | Inequality in access to job opportunities by income decile | Palma ratio | Inequality in travel time to closes hospital

General variables (population, land use, transport) | Accessibility indicators | Organization of the columns with accessibility estimates | 1) Type of accessibility indicator | 2) Type of opportunity / population | 3) Time threshold (only applicable to CMA and CMP estimates) | Examples

Variáveis gerais (população, uso do solo e transporte) | Indicadores de Acessibilidade | Composição da nomeclatura do indicador de acessibilidade | 1) Tipo de indicador de acessibilidade | 2) Oportunidade ou Pessoas | 3) Tempo limite (aplicável apenas para estimativas CMA e CMP) | Exemplos

Installation | Overview of the package | Basic Usage | Data dictionary | Accessibility estimates | Population and land use data | Read only spatial grid data | Note | Acknowledgement | Citation

Download land use data | Spatial distribution of jobs | Spatial distribution of schools | Spatial distribution of healthcare | Map Centers for social assistance (CRAS)

Download population data | Map total population | Map population by income levels | Map population by race

Download accessibility data | Map access to job opportunities | Map access to schools

Download population data | Map total population | Map population by income levels | Map population by race

Download accessibility data | Inequality in access to job opportunities by income decile | Palma ratio | Inequality in travel time to closes hospital

Download land use data | Spatial distribution of jobs | Spatial distribution of schools | Spatial distribution of healthcare | Map Centers for social assistance (CRAS)

Download accessibility data | Map access to job opportunities | Map access to schools

Installation | Overview of the package | Basic Usage | Data dictionary | Accessibility estimates | Population and land use data | Read only spatial grid data | Note | Acknowledgement | Citation

General variables (population, land use, transport) | Accessibility indicators | Organization of the columns with accessibility estimates | 1) Type of accessibility indicator | 2) Type of opportunity / population | 3) Time threshold (only applicable to CMA and CMP estimates) | Examples

Variáveis gerais (população, uso do solo e transporte) | Indicadores de Acessibilidade | Composição da nomeclatura do indicador de acessibilidade | 1) Tipo de indicador de acessibilidade | 2) Oportunidade ou Pessoas | 3) Tempo limite (aplicável apenas para estimativas CMA e CMP) | Exemplos

Currently available decay functions | Binary | Linear | Negative exponential | Inverse power | Stepped | Logistic | Quick demonstration | Using a custom decay function

Installation | Overview of the package | Demonstration on sample data | Data requirements | Minimum travel cost | Cutoff-based cumulative opportunities | Interval-based cumulative opportunities | Gravity measures | Floating catchment area | Spatial availability | Balancing cost | Visualize results | References

Currently available decay functions | Binary | Linear | Negative exponential | Inverse power | Stepped | Logistic | Quick demonstration | Using a custom decay function

Installation | Overview of the package | Demonstration on sample data | Data requirements | Minimum travel cost | Cutoff-based cumulative opportunities | Interval-based cumulative opportunities | Gravity measures | Floating catchment area | Spatial availability | Balancing cost | Visualize results | References

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 Applying the same speed factor to all roads | 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 a spatial polygon | Cleaning up after usage

Larger-than-memory Data | 1. | 2. | 2.1 Combining | 2.2 Using {duckdb} with SQL

1) read_flights(): | 2) read_airports(): | 3) read_aircraft(): | 4) read_airport_movements(): | 5) read_airfares(): | Basic usage | Download flights data: | Download airports data: | Download data of aircraft: | Download data on airport movements: | Download data on data on air fares of domestic flights in Brazil:

Available emission factor models | Visualizing emission factor data | Scaling Emission Factors: making emission factors speed-dependent | Checking gtfs2emis imported data | Learn more | Report a bug

Available emission factor models | Visualizing emission factor data | Scaling Emission Factors: making emission factors speed-dependent | Checking gtfs2emis imported data | Learn more | Report a bug

Tire | Brake | Road Wear | Viewing Emissions | a) Preparing the data | b) Creating spatial grid | c) Generating spatial and temporal patterns | References | Report a bug

Tire | Brake | Road Wear | Viewing Emissions | a) Preparing the data | b) Creating spatial grid | c) Generating spatial and temporal patterns | References | Report a bug

Introduction | Loading data | Converting to GPS-like format | Methodological note | Final remarks

Installation | Demonstration on sample data | Data input | Calculating UCI | Formal definition of UCI | Bootstraping Vmax | References

Installation | Demonstration on sample data | Data input | Calculating UCI | Formal definition of UCI | Bootstraping Vmax | References

Installation | Usage | extract() | tags_filter() | show_content() | Acknowledgement

Installation | Usage | extract() | tags_filter() | show_content() | Acknowledgement

1. Introduction | 2. Installation | 3. Data requirements | 4. Package Overview | 5. Demonstration of sample data | 5.1 Transport model | 5.2 Fleet data | 5.3 Emission model | 6 Analyzing emission estimates | 6.1 Total emissions by pollutant | 6.2 Total emissions by vehicle type | 6.3 Total emissions by time of the day | 6.4 Spatial distribution of emissions | Learn more | Report a bug

1. Introduction | 2. Installation | 3. Data requirements | 4. Package Overview | 5. Demonstration of sample data | 5.1 Transport model | 5.2 Fleet data | 5.3 Emission model | 6 Analyzing emission estimates | 6.1 Total emissions by pollutant | 6.2 Total emissions by vehicle type | 6.3 Total emissions by time of the day | 6.4 Spatial distribution of emissions | Learn more | Report a bug