Uncovering patterns in high dimensional spatial data

One of the best things about spatial data is that it allows us to join a universe of data through a common index of space and time. Tell me the location and time of an event and I can tell you a host of other information that occurred there. How do we start to uncover patterns in this vast array of data? What variables in the strata of spatial datasets are actually relevant for the question we are interested in? We can't visualize more than 2 or 3 variables on a map so we need to rely more on analytical methods to uncover patterns.

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Endangered Plant Site Probability Analysis for UAS Flight Planning

Conservation organizations must locate populations of endangered species in order to monitor their welfare. Accurate endangered species surveys are an important part of site assessments used when acquiring development permits. However, traditional methods of surveying endangered species locations are time and resource intensive and are subject to an individual’s interpretation. Additionally, habitat studies often lack a geospatial analysis component.

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