Access to Opportunities Project:

The flagship project of the AOP-Lab is the “Access to Opportunities Project,” which generates high spatial resolution estimates of access to employment, public health services, education, and social welfare by transportation mode for Brazil's largest cities.

Goals:

The project has three goals:

1. To estimate people's access to employment opportunities, health and education services by transport mode in Brazil's largest urban areas

2. To create open datasets to monitor urban accessibility conditions in Brazilian cities on an annual basis

3. To build research networks to use these datasets in comparative studies and in the planning and evaluation of public policies

Results:

The Access to Opportunities Project (AOP) brings annual estimates of access to jobs, health, education and social welfare services by transport mode, as well as data on the spatial distribution of population and land use activities at a fine spatial resolution for the 20 largest cities in Brazil (see figure below). To do so, the project combines data from administrative records, sample surveys, satellite imagery and collaborative mapping to calculate accessibility levels at a high spatial resolution and disaggregated by socioeconomic groups according to income level, age, sex and race. Methodological details can be found in the annual reports of the project and the data can be downloaded from here.

Methodology:

The methodology of the project involves 4 main steps:

1. First, each city is divided using a hexagonal spatial grid where each cell has an area of 0.11 km².

2. The hexagonal grid is then used to spatially aggreagate population data from the national census, administrative records with the location of formal jobs (low-, medium- and high-qualification jobs), public schools (early childhood, primary and high school education), publich health (low, medium and high complexity medical care) and social welfare services.

3. In the third step, data on public transport (GTFS format), on topography, street network and historical traffic speed are combined to calculate travel-time estimates between every pair of hexagon cells by transport mode. Travel time estimates were calculated for cars using ArcGIS Pro with historical traffic speed data, while travel time estimates for walking, cycling and public transporte were calculated using r5r, multimodal transportation network analysis tool. The r5r tool considers door-to-door travel-time estimates, including in-vehicle times, walking, waiting as well as transfer times.

4. The results of steps (2) and (3) are combined to calculate accessibility levels by transport mode. The project, currently includes three types of accessibility indicators:

• Minimum travel time to the closest activity
• Active cumulative opportunity measure with the number of activities in the city that are accessible within a given time threshold.
• Passive cumulative opportunity measure , indicating by how many people each destination can be reached within a given time threshold.