This lab is about visualizing geospatial data via maps with regions that encode values such as tree densities.
This map is shows the different neighborhood of Trento, separating them as polygons. The color inside the polygon encodes the number of trees present in that region. We chose to discretize the colorbar into a number of colors equal to the number of regions. On hover, a tooltip shows more info (name of neighborhood, number of trees, area). The central parts look to have more trees, probably because the data collection was more intensive there.
Exactly like the map above, but showing the total canopy cover divided the region area. The results look largely the same: with a more similar number of trees, we could have seen differences if one neighborhood had many large trees compared to others.
Here the color encodes the total oxygen production by neighborhood: here the values look more even: maybe the less-populated regions have trees with a bigger oxygen production.
Each tree is represented by a dot on the geographical location on the map. We can see geometric organizations (probably parks where trees are neatly organized). Each dot has a 0.7 opacity, but since most trees' dots overlap with others (since they are close), the color looks as bright as full opacity.
As above, but each dot is bigger, full opacity and encodes the tree type. It looks like Gardolo has a zone with very neatly organized trees, all in the top-9 chart.