# Geostatistics Exploratory Analysis

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1 Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Master of Science in Geospatial Technologies Geostatistics Exploratory Analysis Carlos Alberto Felgueiras 1

2 Geostatistics Exploratory Data Analysis Contents EDA and ESDA Univariate Description Bivariate Description 2

3 The Geostatistical Process General View The Geostatistical Process Region of Study Interpolation kriging Estimated Surface of the phenomenon of interest Structural Analysis Conditional Simulation Realizations Fields Exploratory Analysis Derived Scenarios Uncertainty Maps 3

4 Geostatistics Exploratory Data Analysis What is Exploratory Data Analysis (EDA)? Aim is to identify data properties for purposes of: - pattern detection in data (homogeneity, heterogeneity,.) - hypothesis formulation from data (symmetry, gaussian,..) - some aspects of model assessment (e.g.goodness of fit, identifying data effects on model fit). Analysis are based on: -the use of graphical and visual methods and -the use of numerical techniques that are statistically robust, i.e., not much affected by extreme or atypical data values. Emphasis on descriptive methods rather than formal hypothesis testing. Importance of staying close to the original data in the sense of using simple, intuitive methods. 4

5 Geostatistics Exploratory Spatial Data Analysis What is Exploratory Spatial Data Analysis (ESDA)? extension of EDA to detect spatial properties of data. Need additional techniques to those found in EDA for: - detecting spatial patterns in data - formulating hypotheses based on the geography of the data - assessing spatial models. important to be able to link numerical and graphical procedures with the map need to be able to answer the question: where are those cases on the map?. With modern graphical interfaces this is often done by 'brushing' - for example cases are identified by brushing the relevant part of a boxplot, and the related regions are identified on the map. This lecture only covers the case of attribute data attached to irregular areal units because we want to focus on the range of analytical tools required for ESDA and their provision using GIS. 5

6 EDA/ESDA Data Distribution in Space Sampling Spatial sampling involves determining a limited number of locations in a geo-space for faithfully measuring phenomena that are subject to dependency and heterogeneity. Dependency suggests that since one location can predict the value of another location, we do not need observations in both places. Heterogeneity suggests that this relation can change across space, and therefore we cannot trust an observed degree of dependency beyond a region that may be small. Basic spatial sampling schemes include random, clustered and systematic. Each sample point α is represented by (x,y,z) (x,y) is the 2-d space location z is the attribute value 6

7 Exploratory Data Analysis - EDA Univariate Description Frequency Tables and Histograms Cumulative Frequency Tables and Histograms PDFs and CDFs Normal Probability Plots Summary Statistics Measures of location of the distributions Measures of spread of the distributions Measures of shape of the distributions Detection of rough components 7

8 EDA Univariate Description Frequency Tables and Histograms Continuous Variables Example of Exam Scores Group Count 0 - < < < < < < < < < Categorical Variables Favorite Sports to watch Group Count Basketball 123 Football 282 Hokey 15 Others 22 Important: The count (frequency) of each group devided by the total population give the pdf (probability distribution function) of the variable. 8

9 EDA Univariate Description Frequency Tables and Histograms a simple example Ordered Sample Sets Set A Set B Histograms of Sets A and B n A = 8 and n B = 7 9

10 EDA Univariate Description Cumulative Frequency Tables and Histograms Continuous Variables Height Frequency Cumulative Values Frequency <=3 1 1 <= <= <= <= <= <= Categorical Variables Height value A = Basketball B = Basketball + Football C = Basketball + Football + Hokey C = Basketball + Football + Hokey + Others Cummulative Frequency Important: The cummulative frequency values devided by the total population give the cdf (cumulative distribution function) of the variable. A B C D Classes 10

11 EDA Univariate Description PDFs and CDFs - The Normal or Gaussian Distribution Graphically the normal distribution is best described by a 'bell-shaped' curve. This curve is described in terms of the point at which its height is maximum (its 'mean') and how wide it is (its 'standard deviation'). It is a parametric distribution because it can be totally described by the two parameters, the mean and the standard deviation It can be described by: It is mostly used in equations development and hypothesis (to be confirmed) for theoretical developments. 11

12 EDA Univariate Description Normal Probability Plots Some of statistical tools work better, or only work, if the distribution of the data values is close to a Gaussian or Normal distribution. On a Normal Probability Plot the y-axis is scaled in such a way that the cumulative frequencies will plot as a straight line if the distribution is Gaussian. 12

13 EDA Univariate Description Normal Probability Plots a simple example Ordered Sample Sets Set A Set B Normal Probability Plots of Sets A and B Distribution too far from the Gaussian behaviour Distribution more closer to the Gaussian behaviour 13

14 EDA Univariate Description Summary Statistics Measures of Location of the distributions Centers of the distribution Mean is the arithmetic average of α data values. 1 m = µ = z = n n α = 1 z ( α ) 1..5 Cummulative distribution Median Once the data is ordered: z 1 z 2. z n 0 M = z (n+1)/2 if n is odd or M = (z n/2 +z (n/2)+1 )/2 if n is even. q.5 Half of the values are below the median and half are above. M = q.5 Mode The value that occurs most frequently <50 14

15 EDA Univariate Description Summary Statistics - Measures of Location of the distributions Minimum and Maximum The smallest and the largest values, respectively, in the data set. Lower and Upper Quartile In the same way that the median splits the ordered data into halves, the quartis split the increasing sorted data into quarters. The median is the second quartil q or the Q.5 2 Deciles, Percentiles, and Quantiles Deciles split the data into tenths, percentiles split the data into hundredths. Quantiles are the generalization of this idea to any fraction. We represent as q p the p-quantile of the data. Example: q.1 is the first decil and q.25 = Q 1 is the first quartil, and so on. 15

16 EDA Univariate Description Summary Statistics - Measures of Location of the distributions A simple example Sample Sets Set A Set B n A = 8 and n B = 7 Set A Set B Minimum 3 3 Maximum Mean Mode Median Quartil 1 10,75 10,5 Quartil Quartil 3 15,5 14 Obs. The Mean value is sensitive to erratic or extreme values 16

17 EDA Univariate Description Summary Statistics - Measures of Spread of the distributions Variance (second moment) is the average squared difference of the observed values from their mean value. σ 2 = 1 n n 1 α = 1 ( z( α ) z) 2 Standard Deviation The square root of the variance σ = σ 2 Interquartile Range - the difference between the upper and lower quartiles IQR = Q 3 Q 1 = q.75 - q.25 Obs. The interquartile range does not depend on the mean value. 17

18 EDA Univariate Description Summary Statistics - Measures of Spread of the distributions A simple example Sample Sets Set A Set B Set A Set B Mean Variance 1822,57 26,33 Std Var 42,69 5,13 Quartil 1 10,75 10,5 Quartil 3 15,5 14 Q 3 -Q 1 4,75 3,5 Obs. Variance and Standard Deviation are also sensitive to erratic or extreme values Obs. The interquartil value does not depend on the mean value. n A = 8 and n B = 7 18

19 EDA Univariate Description Summary Statistics - Measures of Shape Coefficient of Variation determines if the distribution is large or narrow. It is unit free. CV >> 1 indicate Outliers. CV = σ/m Skweness (coefficient of asymmetry) is a measure of symmetry γ = ( Z z) 3 nσ Curtosis is a measure of how flat the top of a symmetric distribution is when compared to a normal distribution of the same variance. Coefficient of 4 flatness. β = 3 ( Z z) 4 nσ 19

20 EDA Univariate Description Summary Statistics - Measures of Shape of the distributions A simple example Sample Sets Set A Set B Set A Set B Mean Std Var 42,69 5,13 Coef. of Variation 1,58 0,43 Skewness 1,81-0,22 Curtosis ,21 20

21 EDA Univariate Description Detection of rough components Any data value can be thought of as comprising two components : one deriving from some summary measure (smooth) and the other a residual component (rough) DATA = smooth PLUS rough Rough example Outliers: values more than a certain distance above (below) the upper (lower) quartile of the distribution (Haining 1993,201). (Errors or Atypical values) For some data analyses, for the spatial continuity of the data for example, the outliers must be removed. Sample Sets Set A Set B Outliers 21

22 EDA Univariate Description Summary Statistics Meas. of Location, Spread and Shape - Use of SpreadSheets 22

23 EDA Univariate Description EDA An Example with Elevation Data A set of 329 elevations sampled inside São Carlos farm region 23

24 EDA Univariate Description EDA An Example with Elevation data Summary Reports and Histograms 24

25 EDA Univariate Description EDA An Example with Elevation data Summary Reports and Histograms 25

26 EDA Univariate Description EDA An Example with Elevation data NORMAL PROBABILITY PLOTS 26

27 Exploratory Data Analysis Bivariate Description The ScatterPlot Correlation between two variables The covariance value The correlation coefficient Linear Regression Conditional Expectation 27

28 EDA Bivariate Description Objectives Representation of relations/co-relations between two variables Comparing the distributions of the two variables Use of graphics and statistic measurements that summarize the main features of the bivariate relations. 28

29 The Scatterplot EDA Bivariate Description A scatterplot is a useful summary of a set of bivariate data (two variables X and Y), usually drawn before working out a linear correlation coefficient or fitting a regression line. It gives a good visual picture of the relationship between the two variables, and aids the interpretation of the correlation coefficient or regression model. Each unit contributes one point to the scatterplot, on which points are plotted but not joined. The resulting pattern indicates the type and strength of the relationship between the two variables. 29

30 EDA ScatterPlot Examples SCATTERPLOTS Elevation Data Sulphur x Aluminium Data 30

31 EDA Bivariate Description Correlation Between two Variables Considering variables Z i and Z j In a scatterplot we can observe, generally, one of the three patterns: (a) positively correlated: larger values of Z i tend to be associated to larger values of Z j (b) negatively correlated: larger values of Z i tend to be associated to larger values of Z j (c) uncorrelated: the two variables are not related Y Y Y X X (a) (b) (c) X 31

32 EDA Bivariate Description Covariance Value Considering variables Z i and Z j The Covariance between two variables is used itself as a summary statistic of a scatterplot. It measures the joint variation of the two variables around their means. It is computed as: C ij 1 n = σ = ij n α = 1 ( )( ) ( α) ( α) z i m i z j m j The covariance depends on the unit of the variables It is formula can be extended for more than two variables 32

33 EDA Bivariate Description Correlation Coefficient Considering variables Z i and Z j The Correlation Coefficient is most commonly used to summarize the relationship between two variables. It can be calculated from: ρ ij = σ ij σ iσ j = 1 n n α =1 ( ( α ) ) ( α ) z i mi z σ iσ j ( ) j m j The correlation value is units free covariance standardized by the standard deviations The correlation value varies between -1 to 1, ρ ij [ 1,1 ] -1 means that the variables are negatively correlated 0 means that the variables are totally uncorrelated 1 means that the variables are positively correlated 33

34 EDA Bivariate Description Linear Regression Considering variables V x and V Y Assuming that the dependence of one variable on to the other is linear, the linear regression help us to predict one variable if the other is known. In this case the linear equation is given by the straight line representation: y = ax + where the slope, a, and the constant, b, are given by: b a = ρ σ y σ x Y b = m a x m y X 34

35 EDA Bivariate Description Simple Numerical Example Sample Sets Set A Set B ScatterPlot Set A Set B Mean Set B Set A Std Var 7,39 7,01 Covariance -39,63 Skewness -0,875 Linear Regression a =-0,83 (-41,19 o ) b=22,3 n A = n B = 8 35

36 EDA Bivariate Description Conditional Expectation Considering variables V i and V j Alternative when linear regression is not a good fit (curve with bends on it) Consists on calculating the mean value of y for different ranges of x (conditional expectation values) Huge number of very narrow classes give smooth curves Problems can occur for too narrow classes (no points in some ranges of x) 36

37 EDA Summary and Conclusions Summary and Conclusions EDA/ESDA provides a set of robust tools for exploring spatial data, which do not require a knowledge of advanced statistics for their use. GIS are currently only poorly equipped with many of these tools, despite containing the basic functionality to allow them to be implemented. 37

38 Geostatistics EDA END of Presentation 38

39 Questions about EDA 1. Explain, using your own words, the difference between the EDA and ESDA terms. 2. Given the data sets A and B (in the right side) perform Univariate and Bivariate Exploratory Data Analyses on them. Compare your results with the results given by statistical (or not) function that are available in a spreadsheet (excel for example). 3. Write a report with the EDA results and with considerations related to the analysis you have done. Send the report to the professor ( before 12/Oct/ Download, from the geostatistics course in ISEGI online, the Text input data for the first lab and the Lab1 Exercise Creating a DataBase in SPRING. Run the lab1 by yourself. Contact the geostatistics professor by or at his office (room 21, ISEGI building) if you have problems on running the lab. Sample Sets Id. Set A Set B 1 33, ,18 34, , ,4 234, , , , ,2 115,2 39

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