Frequently, with spatial analyses, we receive data in one form that seems quite promising but we need it in another more extensive form. LiDAR is an excellent solution for such problems; LiDAR data is laser scanned either from an airborne platform, such as a fixed-wing plane or helicopter, or from a ground unit. LiDAR devices typically return a cloud of points referencing absolute or relative positions in space. As a raw dataset, they are often not as useful as they are once they have been processed. Many LiDAR datasets are classified into land cover types, so a LiDAR dataset, in addition to having data that contains x, y, and z values for all the points sampled across a space, will often contain LiDAR points that are classified as ground, vegetation, tall vegetation, buildings, and so on.
As useful as this is, the data is intensive, that is, it has discreet points, rather than extensive, as polygon representations of such data would be. This recipe...