spatial statistics

AM-19 - Exploratory data analysis (EDA)
• Describe the statistical characteristics of a set of spatial data using a variety of graphs and plots (including scatterplots, histograms, boxplots, q–q plots)
• Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods
AM-25 - Bayesian methods
• Define “prior and posterior distributions” and “Markov-Chain Monte Carlo”
• Explain how the Bayesian perspective is a unified framework from which to view uncertainty
• Compare and contrast Bayesian methods and classical “frequentist” statistical methods
AM-24 - Outliers
• Explain how outliers affect the results of analyses
• Explain how the following techniques can be used to examine outliers: tabulation, histograms, box plots, correlation analysis, scatter plots, local statistics
AM-23 - Local measures of spatial association
• Describe the effect of non-stationarity on local indices of spatial association
• Decompose Moran’s I and Geary’s c into local measures of spatial association
• Compute the Gi and Gi* statistics
• Explain how geographically weighted regression provides a local measure of spatial association
• Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association
• Compare and contrast global and local statistics and their uses
AM-22 - Global measures of spatial association
• Describe the effect of the assumption of stationarity on global measures of spatial association
• Justify, compute, and test the significance of the join count statistic for a pattern of objects
• Compute the K function
• 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
• Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics
• Compute Moran’s I and Geary’s c for patterns of attribute data measured on interval/ratio scales
• Explain how the K function provides a scale-dependent measure of dispersion
AM-21 - The spatial weights matrix
• Explain how different types of spatial weights matrices are defined and calculated
• Discuss the appropriateness of different types of spatial weights matrices for various problems
• Construct a spatial weights matrix for lattice, point, and area patterns
• Explain the rationale used for each type of spatial weights matrix
AM-19 - Exploratory data analysis (EDA)
• Describe the statistical characteristics of a set of spatial data using a variety of graphs and plots (including scatterplots, histograms, boxplots, q–q plots)
• Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods
AM-25 - Bayesian methods
• Define “prior and posterior distributions” and “Markov-Chain Monte Carlo”
• Explain how the Bayesian perspective is a unified framework from which to view uncertainty
• Compare and contrast Bayesian methods and classical “frequentist” statistical methods