SECTION 5: Data from the Access to Opportunity Project

The purpose of this section is: i) to present the accessibility, land use and socioeconomic data made available by the AOP; and ii) to teach how to download and use this data with the {aopdata} R package.

In the previous chapters, we learned about the concept of accessibility, how this concept is usually translated into quantitative measures and how to estimate accessibility levels using R. However, we often come across situations in which we do not want to calculate accessibility levels ourselves, either because we do not have the resources or data required for this or simply because they have already been calculated by other people. Throughout the next chapters, we will present the accessibility database created and made available by AOP.

AOP is a research initiative led by Ipea with the objective of investigating the urban accessibility conditions and the inequalities in access to opportunities in Brazilian cities. All data produced by the project is made publicly available, including not only accessibility estimates, but also information on the spatial distribution of population, economic activities and public services¹. The data is spatially aggregated into a hexagonal grid indexed by the H3 geospatial indexing system, originally developed by Uber (Brodsky 2018). Each hexagonal cell covers around 0.11 km², an area similar to that covered by a city block, resulting in high spatial resolution analyses and outputs. As shown in Figure 1, accessibility estimates have been produced for the years 2017, 2018 and 2019 and for the 20 largest Brazilian cities, considering different transport modes (walking, cycling, public transport and automobile), times of day (peak and off-peak), population groups (aggregated by income, race, sex and age) and types of activity (jobs, schools, health services and social assistance centers).

Figure 1: Scope of AOP data

Accessibility estimates by public transport were calculated only for cities with good quality GTFS data, which include Belo Horizonte, Campinas, Curitiba, Fortaleza, Goiânia², Porto Alegre, Recife, Rio de Janeiro and São Paulo. Still, in some cases the feeds of some of these cities were either only available for a few years or had quality issues, not being representative of the public transport operations they should be describing. In such cases, accessibility estimates by public transport were not calculated. Figure 2 shows the cities included in the project and the transport modes considered in their accessibility estimates.

Figure 2: Geographic scope of AOP data (cities highlighted by an asterisk do not include accessibility estimates by public transport for all years)

The following tables summarize the data made available by the project. Table 1 describes the urban accessibility dataset.

Table 1: Accessibility indicators calculated in AOP

Indicator (code)	Description	Type of opportunities	Travel time thresholds
Minimum travel time (TMI)	Time to the nearest opportunity	Health, education and social assistance reference centers (CRAS)	Walk (60 minutes); bicycle, public transport and car (120 minutes)
Active cumulative accessibility measure (CMA)	Number of accessible opportunities within a given travel time threshold	Jobs, health, education and CRAS	Walk and bicycle (15, 30, 45 and 60 minutes); public transport and car (15, 30, 60, 90 and 120 minutes)
Passive cumulative accessibility measure (CMP)	Number of people that can access the grid cell within a given travel time threshold	-	Walk and bicycle (15, 30, 45 and 60 minutes); public transport and car (15, 30, 60, 90 and 120 minutes)

Table 2 describes the dataset containing the sociodemographic characteristics of the population and the spatial distribution of opportunities.

Table 2: Data on the sociodemographic characteristics of the population and the spatial distribution of activities aggregated by AOP, by year of reference and data source

Data	Information	Years	Source
Sociodemographic characteristics of the population	Number of people by sex, age and race; average income per capita	2010	Demographic Census from the Brazilian Institute for Geography and Statistics (IBGE)
Education services	Number of public schools by education level (early childhood, primary and secondary education)	2017, 2018, 2019	School Census from the Anísio Teixeira National Institute for Educational Studies and Research (Inep)
Health services	Number of health facilities that serve the Unified Health System (SUS) by complexity level (low, medium and high complexity)	2017, 2018, 2019	National Registry of Health Facilities (CNES) from the Ministry of Health
Economic activity	Number of formal jobs by education level of workers (primary, secondary and tertiary education)	2017, 2018, 2019	Annual Relation of Social Information (RAIS) from the Ministry of Economy
Social welfare services	Number of CRAS	2017, 2018, 2019	Unified Social Assistance System (SUAS) Census from the Ministry of Citizenship

All datasets created by AOP are available for download in the project website and through the {aopdata} R package. The data dictionary can be accessed online³ or with the command aopdata::aopdata_dictionary(lang = "en") in an R session. The chapters in this section provide several examples illustrating how to download the datasets and create visualizations with them in R.

Brodsky, Isaac. 2018. “H3: Uber’s Hexagonal Hierarchical Spatial Index.” Uber Engineering Blog. https://eng.uber.com/h3/.

Pereira, Rafael H. M., Carlos Kauê Vieira Braga, Daniel Herszenhut, Marcus Saraiva, and Diego Bogado Tomasiello. 2022. “Estimativas de acessibilidade a empregos e serviços públicos via transporte ativo, público e privado nas 20 maiores cidades do Brasil em 2017, 2018, 2019.” Texto para Discussão IPEA 2800.

Pereira, Rafael H. M., Daniel Herszenhut, Carlos Kauê Vieira Braga, João Pedro Bazzo, João Lucas Albuquerque Oliveira, João Pedro Parga, Marcus Saraiva, Luiz Pedro Silva, Lucas Warwar, and Diego Bogado Tomasiello. 2022. “Distribuição espacial de características sociodemográficas e localização de empregos e serviços públicos das vinte maiores cidades do Brasil.” Texto para Discussão IPEA 2772 (June): 28. https://doi.org/10.38116/td2772.

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1. The methods used to generate these datasets are presented in detail in two separate publications, one for population and land use data (Pereira, Herszenhut, et al. 2022) and another for accessibility data (Pereira, Braga, et al. 2022).

2. Goiânia’s GTFS covers not only the municipality, but its entire metropolitan region.

3. Available at https://ipeagit.github.io/aopdata/articles/data_dic_en.html.