For this weeks assignment, we were tasked to begin exploring our dataset with some basic exploratory spatial statistics tools from ArcGIS (average nearest neighbor or/and spatial autocorrelation and hot-spot analysis). Since, my underlying problem is to interpret subsurface geologic characteristics throughout the Northern Gulf of Mexico to fill spatial gaps, I need to understand both the distribution of my sampling points (n=13625) as well as the spatial distribution of the subsurface geologic characteristics associated with each sampling point, such as average porosity, initial temperature (°F), and initial pressure (psi). Therefore, to get at the initial distribution of my sampling points, I used average nearest neighbor to identify if the distribution of my sampling points tended to be clustered, random, or dispersed. Results (table 1) showed that my sampling points were significantly clustered, which verifies with the patterns observed visually (figure 1).
However, since I know the distribution of my sampling points, how could I be sure that the spatial pattern of the subsurface geologic characteristics wouldn’t just reflect the clustered sampling distribution? Therefore, I decided to subsample my data points, first to a smaller geographic area Mississippi Canyon Outer Continental Shelf (OCS) lease block (n=397; Figure 2A), and then further subsample those points (using the Create Random Points tool in ArcGIS) to select data points (n=50) to give them a clustered, random, and dispersed spatial distribution (determined using average nearest neighbor; Figure 2B). Then, I ran the spatial autocorrelation tool for each subsample (all Mississippi Canyon, and the clustered, random, and dispersed samples within Mississippi Canyon), which identified that despite the distribution of my sampling points, the values for temperature, pressure, and porosity are significantly clustered (Table 1). The next spatial statistic tool requested to test with our dataset was the hot spot analysis tool. I ran this tool on temperature value for the Mississippi Canyon (n=367) data subsample to identify if there are significant spatial clusters of high and low temperatures values. Results show (Figure 2C) that there are significant clusters of high temperatures (red dots and blue triangles) and low temperature (blue dots and blue triangles). Now, the next step is to being exploring the relationships between different subsurface geologic characteristics and different environmental conditions, such as water depth, subsurface depth, geologic age, etc. to identify any correlations that can used to help fill in spatial gaps of subsurface geologic characteristics throughout the Northern Gulf of Mexico.
