Geolocalização: de endereços para coordenadas espaciais | Processo de matching de endereços | Grau de precisão dos resultados | Precisão | Tipos de resultados | Desvio em metros | Código do setor censitário

Geolocalização reversa: de coordenadas espaciais para endereços

Instalação | Utilização | 1. Geolocalização: de endereços para coordenadas espaciais | 2. Geolocalização reversa: de coordenadas espaciais para endereços | 3. Busca por CEPs | Cache de dados

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

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

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

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

Data Dictionary | Questionnaires | Interview manual

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

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

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:

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