Conducting an ordinary least squared analysis may lead to more information about which factors contribute more to the clustered pattern. The spatial autocorrelation tool indicates that clustering is occurring with regard to the sea surface temperature and chlorophyll values at their respective locations with regard to sea turtles. The critical value (z-score) was less than 2.58 but greater than 1.96 thus suggesting that there is less than 5-percent likelihood that the clustered pattern is a result of random chance.įigure 7: Results of the spatial autocorrelation Moran’s I for chlorophyll-a at the leatherback sea turtle locations.Īs suggested in the hotspot analysis there is clustering of the data. Since the critical value (z-score) was greater than 2.58 there is less than 1-percent likelihood that the clustered pattern is a result of random chance.įigure 6: Sea surface temperature results for Moran’s I tool.Ĭhlorophyll-a : The results of the spatial Autocorrelation tool suggest that the pattern of chlorophyll at each feature location is clustered. Sea Surface Temperature: The results of the spatial autocorrelation tool suggest that the pattern of sea surface temperature at each feature location is clustered. I selected a 500km distance (smaller distances were too small for the study site). The conceptualization of spatial relationships method used was the inverse distance and the Euclidean distance measure was used for the distance method. I tested the spatial autocorrelation of chlorophyll-a and sea surface temperature at each feature location. This tool “Extracts cell values at locations specified in a point feature class from one or more rasters, and records the values to the attribute table of the point feature class.”įigure 3: Sea turtle locations in the Gulf of Mexico, derived from įigure 4: Chlorophyll-a(mg/m^3) data for January 2005 within the Gulf of Mexico, derived from NOAA.įigure 5: Sea Surface Temperautre (Celsius) data for January 2005 within the Gulf of Mexico, derived from NOAA. (ESRI image)īefore conducting this test, I sampled the SST and the CHL-a values at each of the feature locations (sea turtle locations) using the Extract Multi Values to Points tool. It will also provide a scale for the significance of the p-value and critical value for the z-score.įigure 2: Sample output for the Moran’s I tool. This report will also include the Moran’s Index value, z-score, p-value. Upon opening the HTML report for the Moran’s I results you will see a graph showing how the tool calculated the data and whether or not the data is dispersed, random, or clustered. The output of the Moran’s I tool can be found in the results section of ArcGIS. The tool generates a Z-score and p-value which helps evaluate the significance of the Moran’s index.įigure 1: Calculations used for the Moran’s I tool. Positive spatial autocorrelation will show values that are clustered. The Moran’s I index will be a value between -1 and 1. The spatial autocorrelation tool utilizes a multidimensional and multi-directional factors. Spatial Autocorrelation (Moran’s I): This tool measures spatial autocorrelation using feature locations and feature values simultaneously.
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