Geospatial Allocation based on Vaccine Distribution

In this example, we want to find the 5 hospitals out of all available hospitals that can distribute a vaccine the most efficient to all citizens.

We first get all required information such as the available hospitals and the number of citizens on the block group level from the US Census. To simplify the problem, we compute the centroid as representative of each block group. To estimate the distances, we compute the Euclidean distance between each available hospital and the block group representatives. All this information if than used in the P-median node to minimize the distances between the block group representatives and the 5 potential hospitals. Finally, the assignment of each hospital to each block group representative is visualized on an interactive map.

Geospatial Analytics is fully developed in Python, e.g. the Geopandas library, which was heavily used to write the nodes. All the nodes provided with the extension are the perfect toolkit to apply geospatial technologies in a no-code/low-code way, so also beginners can benefit from this kind of analysis.

External resources

• Center for Geographic Analysis
• Geopandas Documentation