240 - Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends
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Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends
Spatial autocorrelation evaluates how things which are closer in space tend to have similar attributes. This is a common phenomenon in environmental variables which are continuous in space. For instance, temperature, soil moisture content, air quality and rainfall are all continuous in space. This idea is based on Tobler’s law of geography: “everything is related to everything but near things are more related”. Global measures of spatial association estimates the overall index of spatial autocorrelation, also called spatial clustering. Thus, it measures whether clustering is apparent throughout the study region but do not identify the location of clusters. Common global measures include the Moran’s Index and Geary’s C. These have increasing applications in domains like environmental science, agriculture, epidemiology, climate studies etc.