Geospatial Heatmap
The "Heatmap in GIS" webpage explains that heat mapping is a way to display the geographical concentration of a particular occurrence from a GIS standpoint. The complete article can be found in the "External resources" section.
We aim to map out fires over one hundred acres in the Washington state region to gain insight into the areas most affected by wildfires.
To use the workflow, download and run it on the KNIME Analytics Platform. For optimal performance using the Geospatial Analytics extension, make sure you are using version 4.7.4 or higher.
The input data has been saved in the workflow data area, simply execute the components "Read Dataset" to obtain the necessary data.
We use the "Rule-based Row Filter" node to exclude fires that have affected less than one hundred acres. The resulting table has 405 entries which we will display on a Heatmap. Before the visualization, we use the "Column filter" node to select specific features like "ACRES_BURNED" and "geometry".
Now is the time for the Heatmap, as important parameters to set up:
Colour map: We opted for a yellow-to-red scale of nine clusters named "YlOrRd_09" for our case. This bi-colour scale was chosen to distinguish areas with low and high fire density more clearly.
Radius: increase or decrease the radius from each point. When there are more points, the area becomes denser, and the colour appears darker (depending on the colour scheme). We have set up this value to fifteen.
Minimum opacity and Blur are set up to 0.3 and 20, respectively. To achieve the "Heatmap" effect, we set the minimum opacity and blur values. We aim to display low-density areas in yellow. Thus, we assign some opacity to them, 0.3. Additionally, we want to emphasize the density areas with a low blur value, 20, with the lowest value (0) being the darkest.
The Heatmap node has an important feature that allows the user to set a weight column for the calculation. In our case, we have selected the burned acres by the fire to create a weighted Heatmap.
You can open both visualizations and compare how they look with and without the weighted column.