Bailey&Gatrell(Chapter 5)

Bailey&Gatrell(Chapter 5) - Continuous Data...

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1 Analysis of Spatially Continuous Data Bailey and Gatrell Lecture 13 October 20, 2009 Chapter 5 1 The locations are simply sites at which attribute values have been recorded within a region Continuous Data Analysis Focus is on patterns in the attribute values not locations as in the analysis of point patterns Attributes are conceptually spatially continuous. Examples include observations on rainfall, temperature, salinity, air quality variables such as ozone, soil variables such as permeability, conductivity, pH, 2 The Data Observations on a spatially stochastic process that varies continuously over a region R and has been sampled at fixed point locations s i .   R s s Y , Referred to as ) ( i s y for random variable ) ( i s Y Shortened to or Y (si) for the vector Y = (Y (s1) ,…Y (sn) ) for the random variable Y i y Often referred to as geostatistical data 3 Analysis Objectives: Infer the nature of spatial variation in an attribute over the whole of a region R based on sampled point values. Examine first order effects – variations in the mean value of surface (large scale), and second order effects (spatial dependence between values at any 2 locations) Model the pattern of variability of an attribute and determine factors that might relate to it Obtain predictions of a value at un-sampled locations dependence between values at any 2 locations). 4
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2 Consider first and second order effects Continuous Data Analysis Develop descriptions that capture global trends as well as local variability  s s Y E ) (   ) ( ), ( j i s Y s Y COV Spatial dependence between and Y Y Propose models consisting of two components First order component – representing large (coarse) scale variation Second order component – representing fine scale spatial dependence Spatial dependence between and ) ( i s ) ( j s 5 Visualizing Spatially Continuous Data Use symbols that will represent the information on the data values Proportional circles or rectangles are often used The size of the circle is proportional to the data value Or height of the rectangle is proportional to the data value e.g. radius equal to the square root of data values Colors can be used to reinforce the same data value or add a different variable 6 7 8
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3 Visualizing Spatially Continuous Data When mapping symbols to classes the number of classes and the type of class interval “influence the message” Larger numbers of data values typically require more classes to cover the range Rule of thumb Number of classes equal to 1+ 3.3 log
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This note was uploaded on 02/15/2012 for the course GEO 6938 taught by Professor Staff during the Summer '08 term at University of Florida.

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Bailey&Gatrell(Chapter 5) - Continuous Data...

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