2020-09-21, 18:10–18:40, Room 1
This analysis will try to get further insight on Bolivia's 2019 polemic results by applying standard ESDA methods with Python's Pysal library. To our knowledge, it would be the first work on this topic focusing on the geospatial dimension of the data
For this i intend to work on data from the oficial source (Bolivia's Organo Electoral Plurinacional) on the Recinto level of aggregation, excluding data from outside of Bolivia. This dataset is tabular, so it will be georeferenced with help of a shapefile of Bolivia's Education Buildings (point type).
On top of this, without any previous assumption, we will crush the data with (some of) Pysal's tools, in the hope that the data will "speak for itself" louder in the geospatial realm than in more traditional approaches.
For this we will deploy and use a Jupyter Notebook environment as a frontal to an infrastructure composed of an analysis and viz server, an OGC-WMS server, and a geospatial PostGIS database