In this example, we want to visually represent the population distribution by ethnic identification in the city of New York. We get this information from the US2020 Census, where people can place themselves into the following categories: White, African American, Latino, Asian and Native American.
The goal here is to generate a Geographic Heatmap to easily visualize the composition of the Manhattan inhabitants by their ethnic identification.
We use the Geospatial View node to create this visual map and KNIME nodes to build a dynamic data application where you can select the percentage of people of one ethnic identity over the total, to see how they are geographically distributed.
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.
Workflow 
Geospatial View
External resources
Used extensions & nodes
Created with KNIME Analytics Platform version 4.7.0
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SDL, Harvard, Cambridge US
Version 1.0.0
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KNIME AG, Zurich, Switzerland
Version 4.7.0
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KNIME AG, Zurich, Switzerland
Version 4.7.0
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KNIME AG, Zurich, Switzerland
Version 4.7.0
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