1. Analytics models#
The backbone of the analytics pipeline consists in building road network models from Open-Street Maps (OSM) data and incorporates population information from the raster layers available at the Humanitarian Data Exchange.
Building Road network model
Incorporating population data
Importing raw population data
Aggregating population into analysis zones
Importing population pyramid data
Incorporating amenity and building data
- At the end of the creation of the analytics models, the user will have all their data inside an
aequilibrae model, which is a Python-native
transport modelling software. In turn, all files used in AequilibraE are open-format (SQLite, Spatialite) and can be used by virtually any current data and GIS software/platform available.
1.1. Building the road network model#
The first step in the analytics setup process is the development of the Road Network model from OSM data.
This step includes the following sub-steps:
Downloading and interpreting (parsing) the OSM network
Downloading the country borders from Open-Street Maps
Making sure that only links from within the country borders are kept in the model
Veryfing if the network can be used for computing routes from two arbitrary points
1.2. Incorporating population data#
As mentioned above, the population information we use in this analytics pipeline are obtained directly from the Humanitarian Data Exchange and come on a raster (i.e. image) format, and it is therefore inadequate for performing the type of analytics we are interested in.
This results on a process that has is significantly more complex when compared to the creation of the Road network model, so it is comprised of 3 different Jupyter Notebooks that allow the user to import the raw population data and to aggregate it into a level of geographic detail that is compatible with the specific needs.
The importing of raw population data consists in getting the country population information from a raster image or file and importing them to the analitics data model as a geographic layer of points, each one of which representing a pixel of the original image and carrying the population attributed to that pixel.
This process includes the following steps:
Converting the population layer image to points that are contained within the country borders for our model
Importing this point layer into a raw_population layer inside our model database
1.3. Importing amenity and buildings#
The amenity and building information we use is obtained directly from the OSM. Both amenity and building information provides us useful information regarding land-use. Later, we can use this information as an input for the trip generation model.